Systems, methods, and devices for automatic signal detection based on power distribution by frequency over time within an electromagnetic spectrum

ABSTRACT

Systems, methods, and apparatus for automatic signal detection in a radio-frequency (RF) environment are disclosed. At least one node device is in a fixed nodal network. The at least one node device is operable to measure and learn the RF environment in a predetermined period based on statistical learning techniques, thereby creating learning data. The at least one node device is operable to create a spectrum map based on the learning data. The at least one node device is operable to calculate a power distribution by frequency of the RF environment in real time or near real time, including a first derivative and a second derivative of fast Fourier transform (FFT) data of the RF environment. The at least one node device is operable to identify at least one signal based on the first derivative and the second derivative of FFT data.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application relates to and claims priority from the followingapplications. This application is a continuation-in-part of U.S. patentapplication Ser. No. 16/275,575 filed Feb. 14, 2019. U.S. patentapplication Ser. No. 16/275,575 claims the benefit of U.S. ProvisionalApplication 62/632,276 filed Feb. 19, 2018. U.S. patent application Ser.No. 16/275,575 also claims priority from and is a continuation-in-partof U.S. patent application Ser. No. 16/274,933 filed Feb. 13, 2019,which is a continuation-in-part of U.S. patent application Ser. No.16/180,690 filed Nov. 5, 2018, which is a continuation-in-part of U.S.patent application Ser. No. 15/412,982 filed Jan. 23, 2017. U.S. patentapplication Ser. No. 16/180,690 also claims priority from U.S.Provisional Patent Application No. 62/722,420 filed Aug. 24, 2018. U.S.patent application Ser. No. 16/274,933 also claims the benefit of U.S.Provisional Application 62/632,276 filed Feb. 19, 2018. This applicationis also a continuation-in-part of U.S. patent application Ser. No.16/360,841 filed Mar. 21, 2019, which is a continuation of U.S. patentapplication Ser. No. 15/681,521 filed Aug. 21, 2017 and issued as U.S.Pat. No. 10,244,504, which is a continuation-in-part of U.S. patentapplication Ser. No. 15/478,916, filed Apr. 4, 2017. Each of theabove-mentioned applications is incorporated herein by reference in itsentirety.

BACKGROUND OF THE INVENTION 1. Field of the Invention

The present invention relates to spectrum analysis and management forradio frequency (RF) signals, and more particularly for automaticallyidentifying signals in a wireless communications spectrum based ontemporal feature extraction.

2. Description of the Prior Art

Generally, it is known in the prior art to provide wirelesscommunications spectrum management for detecting devices for managingthe space. Spectrum management includes the process of regulating theuse of radio frequencies to promote efficient use and gain net socialbenefit. A problem faced in effective spectrum management is the variousnumbers of devices emanating wireless signal propagations at differentfrequencies and across different technological standards. Coupled withthe different regulations relating to spectrum usage around the globeeffective spectrum management becomes difficult to obtain and at bestcan only be reached over a long period of time.

Another problem facing effective spectrum management is the growing needfrom spectrum despite the finite amount of spectrum available. Wirelesstechnologies have exponentially grown in recent years. Consequently,available spectrum has become a valuable resource that must beefficiently utilized. Therefore, systems and methods are needed toeffectively manage and optimize the available spectrum that is beingused.

Most spectrum management devices may be categorized into two primarytypes. The first type is a spectral analyzer where a device isspecifically fitted to run a ‘scanner’ type receiver that is tailored toprovide spectral information for a narrow window of frequencies relatedto a specific and limited type of communications standard, such ascellular communication standard. Problems arise with these narrowlytailored devices as cellular standards change and/or spectrum usechanges impact the spectrum space of these technologies. Changes to thesoftware and hardware for these narrowly tailored devices become toocomplicated, thus necessitating the need to purchase a totally differentand new device. Unfortunately, this type of device is only for aspecific use and cannot be used to alleviate the entire needs of thespectrum management community.

The second type of spectral management device employs a methodology thatrequires bulky, extremely difficult to use processes, and expensiveequipment. In order to attain a broad spectrum management view andcomplete all the necessary tasks, the device ends up becoming aconglomerate of software and hardware devices that is both hard to useand difficult to maneuver from one location to another.

While there may be several additional problems associated with currentspectrum management devices, at least four major problems existoverall: 1) most devices are built to inherently only handle specificspectrum technologies such as 900 MHz cellular spectrum while not beingable to mitigate other technologies that may be interfering or competingwith that spectrum, 2) the other spectrum management devices consist oflarge spectrum analyzers, database systems, and spectrum managementsoftware that is expensive, too bulky, and too difficult to manage for auser's basic needs, 3) other spectrum management devices in the priorart require external connectivity to remote databases to performanalysis and provide results or reports with analytics to aid inmanagement of spectrum and/or devices, and 4) other devices of the priorart do not function to provide real-time or near real-time data andanalysis to allow for efficient management of the space and/or devicesand signals therein.

In today's complex RF environment, to detect a signal can be difficult,especially for those that are less consistent, with low power levels, orburied in easily identified signals. These signals cannot be detected bya radio gear in the prior art. Some devices in the prior art can doautomatic violation detection by creating a rough channel mask based onexternal database, for example the FCC database and comparing thespectrum against that channel mask and detecting signals that violatethe channel mask. However, these devices cannot detect signals that arenot in the external database.

Examples of relevant prior art documents include the following:

U.S. Pat. No. 8,326,313 for “Method and system for dynamic spectrumaccess using detection periods” by inventors McHenry, et al., filed Aug.14, 2009, discloses methods and systems for dynamic spectrum access(DSA) in a wireless network. A DSA-enabled device may sense spectrum usein a region and, based on the detected spectrum use, select one or morecommunication channels for use. The devices also may detect one or moreother DSA-enabled devices with which they can form DSA networks. A DSAnetwork may monitor spectrum use by cooperative and non-cooperativedevices, to dynamically select one or more channels to use forcommunication while avoiding or reducing interference with otherdevices. Classification results can be used to “learn” classificationsto reduce future errors.

U.S. Publication No. 2013/0005240 for “System and Method for DynamicCoordination of Radio Resources Usage in a Wireless Network Environment”by inventors Novak, et al., filed Sep. 12, 2012, discloses anarchitecture, system and associated method for dynamic coordination ofradio resource usage in a network environment. In one aspect, a relaycommunication method comprises detecting, by a first wireless mobiledevice, sensory data associated with multiple radio channels relative toat least one radio element in a sensing area of the first wirelessmobile device. If the first wireless mobile device is out of range of awide area cellular network, a short-range wireless communication path isestablished with a second wireless mobile device having a wide areacellular communication connection. The sensory data is transmitted bythe first wireless mobile device to the second wireless mobile devicefor reporting to a network element via a wide area cellular networkserving the second wireless mobile device. The sensory data areprocessed by sensing elements and sent to a distributed channeloccupancy and location database (COLD) system. The sensory data isupdated dynamically to provide a real-time view of channel usage.

U.S. Pat. No. 8,515,473 for “Cognitive radio methodology, physical layerpolicies and machine learning” by inventors Mody, et al., filed Mar. 6,2008, discloses a method of cognitive communication for non-interferingtransmission, wherein the improvement comprises the step of conductingradio scene analysis to find not just the spectrum holes or Whitespaces; but also to use the signal classification, machine learning,pattern-matching and prediction information to learn more things aboutthe existing signals and its underlying protocols, to find the Grayspace, hence utilizing the signal space, consisting of space, time,frequency (spectrum), code and location more efficiently.

U.S. Publication 2013/0217450 for “Radiation Pattern Recognition Systemand Method for a Mobile Communications Device” by inventors Kanj, etal., filed Nov. 26, 2010, discloses a radiation pattern recognitionsystem and method for a wireless user equipment (UE) device wherein aset of benchmark radiation patterns are matched based on the wireless UEdevice's usage mode. In one aspect, the wireless UE device includes oneor more antennas adapted for radio communication with atelecommunications network. A memory is provided including a database ofbenchmark radiation patterns for each of the one or more antennas in oneor more usage modes associated with the wireless UE device. A processoris configured to execute an antenna application process for optimizingperformance of the wireless UE device based at least in part upon usingthe matched set of benchmark radiation patterns.

U.S. Pat. No. 8,224,254 for “Operating environment analysis techniquesfor wireless communication systems” by inventor Simon Haykin, filed Oct.13, 2005, describes methods and systems of analyzing an operatingenvironment of wireless communication equipment in a wirelesscommunication system. A stimulus in the operating environment at alocation of the wireless communication equipment is sensed and linearlyexpanded in Slepian sequences using a multitaper spectral estimationprocedure. A singular value decomposition is performed on the linearlyexpanded stimulus, and a singular value of the linearly expandedstimulus provides an estimate of interference at the location of thewireless communication equipment. The traffic model, which could bebuilt on historical data, provides a basis for predicting future trafficpatterns in that space which, in turn, makes it possible to predict theduration for which a spectrum hole vacated by the incumbent primary useris likely to be available for use by a cognitive radio operator. In awireless environment, two classes of traffic data pattern aredistinguished, including deterministic patterns and stochastic patterns.

U.S. Pat. No. 5,393,713 for “Broadcast receiver capable of automaticstation identification and format-scanning based on an internal databaseupdatable over the airwaves with automatic receiver locationdetermination” by inventor Pierre R. Schwob, filed Sep. 25, 1992,describes a broadcasting system capable of automatically orsemi-automatically updating its database and using the database toidentify received broadcasting stations, and search for stationsaccording to user-chosen attributes and current data. The receiver iscapable of receiving current location information within the receiveddata stream, and also of determining the current location of thereceiver by using a received station attribute. The invention providesan automatic or quasi-automatic data updating system based on subcarriertechnology or other on-the-air data transmission techniques.

U.S. Pat. No. 6,741,595 for “Device for enabling trap and trace ofinternet protocol communications” by inventors Maher, III, et al., filedJun. 11, 2002, describes a network processing system for use in anetwork and operable to intercept communications flowing over thenetwork, the network passing a plurality of data packets, which form aplurality of flows, the network processing system comprising: a learningstate machine operable to identify characteristics of one or more of theflows and to compare the characteristics to a database of knownsignatures, one or more of the known signatures representing a searchcriteria, wherein when one or more characteristics of one or more of theflows matches the search criteria the learning state machine interceptsthe flow and replicates the flow, redirecting the replication to aseparate address.

U.S. Pat. No. 7,676,192 for “Radio scanner programmed from frequencydatabase and method” by inventor Wayne K. Wilson, filed Jan. 7, 2011,discloses a scanning radio and method using a receiver, a channel memoryand a display in conjunction with a frequency-linked descriptordatabase. The frequency-linked descriptor database is queried using ageographic reference to produce a list of local radio channels thatincludes a list of frequencies with linked descriptors. The list ofradio channels is transferred into the channel memory of the scanner,and the receiver is sequentially tuned to the listed frequenciesrecalled from the list of radio channels while the corresponding linkeddescriptors are simultaneously displayed.

U.S. Publication 2012/0148069 for “Coexistence of white space devicesand wireless narrowband devices” by inventors Chandra, et al., filedDec. 8, 2010, discloses architecture enabling wireless narrowbanddevices (e.g., wireless microphones) and white space devices toefficiently coexist on the same telecommunications channels, while notinterfering with the usability of the wireless narrowband device. Thearchitecture provides interference detection, strobe generation anddetection and, power ramping and suppression (interference-freecoexistence with spectrum efficiency). The architecture provides theability of the white space device to learn about the presence of themicrophone. This can be accomplished using a geolocation database,reactively via a strober device, and/or proactively via the stroberdevice. The strober device can be positioned close to the microphonereceiver and signals the presence of a microphone to white space deviceson demand. The strober device takes into consideration the microphone'scharacteristics as well as the relative signal strength from themicrophone transmitter versus the white space device, in order to enablemaximum use of the available white space spectrum.

U.S. Pat. No. 8,326,240 for “System for specific emitter identification”by inventors Kadambe, et al., filed Sep. 27, 2010, describes anapparatus for identifying a specific emitter in the presence of noiseand/or interference including (a) a sensor configured to sense radiofrequency signal and noise data, (b) a reference estimation unitconfigured to estimate a reference signal relating to the signaltransmitted by one emitter, (c) a feature estimation unit configured togenerate one or more estimates of one or more feature from the referencesignal and the signal transmitted by that particular emitter, and (d) anemitter identifier configured to identify the signal transmitted by thatparticular emitter as belonging to a specific device (e.g., devicesusing Gaussian Mixture Models and the Bayesian decision engine). Theapparatus may also include an SINR enhancement unit configured toenhance the SINR of the data before the reference estimation unitestimates the reference signal.

U.S. Pat. No. 7,835,319 for “System and method for identifying wirelessdevices using pulse fingerprinting and sequence analysis” by inventorSugar, filed May 9, 2007, discloses methods for identifying devices thatare sources of wireless signals from received radio frequency (RF)energy, and, particularly, sources emitting frequency hopping spreadspectrum (FHSS). Pulse metric data is generated from the received RFenergy and represents characteristics associated thereto. The pulses arepartitioned into groups based on their pulse metric data such that agroup comprises pulses having similarities for at least one item ofpulse metric data. Sources of the wireless signals are identified basedon the partitioning process. The partitioning process involvesiteratively subdividing each group into subgroups until all resultingsubgroups contain pulses determined to be from a single source. At eachiteration, subdividing is performed based on different pulse metric datathan at a prior iteration. Ultimately, output data is generated (e.g., adevice name for display) that identifies a source of wireless signalsfor any subgroup that is determined to contain pulses from a singlesource.

U.S. Pat. No. 8,131,239 for “Method and apparatus for remote detectionof radio-frequency devices” by inventors Walker, et al., filed Aug. 21,2007, describes methods and apparatus for detecting the presence ofelectronic communications devices, such as cellular phones, including acomplex RF stimulus is transmitted into a target area, and nonlinearreflection signals received from the target area are processed to obtaina response measurement. The response measurement is compared to apre-determined filter response profile to detect the presence of a radiodevice having a corresponding filter response characteristic. In someembodiments, the pre-determined filter response profile comprises apre-determined band-edge profile, so that comparing the responsemeasurement to a pre-determined filter response profile comprisescomparing the response measurement to the pre-determined band-edgeprofile to detect the presence of a radio device having a correspondingband-edge characteristic. Invention aims to be useful in detectinghidden electronic devices.

U.S. Pat. No. 8,369,305 for “Correlating multiple detections of wirelessdevices without a unique identifier” by inventors Diener, et al., filedJun. 30, 2008, describes at a plurality of first devices, wirelesstransmissions are received at different locations in a region wheremultiple target devices may be emitting, and identifier data issubsequently generated. Similar identifier data associated with receivedemissions at multiple first devices are grouped together into a clusterrecord that potentially represents the same target device detected bymultiple first devices. Data is stored that represents a plurality ofcluster records from identifier data associated with received emissionsmade over time by multiple first devices. The cluster records areanalyzed over time to correlate detections of target devices acrossmultiple first devices. It aims to lessen disruptions caused by devicesusing the same frequency and to protect data.

U.S. Pat. No. 8,155,649 for “Method and system for classifyingcommunication signals in a dynamic spectrum access system” by inventorsMcHenry, et al., filed Aug. 14, 2009, discloses methods and systems fordynamic spectrum access (DSA) in a wireless network wherein aDSA-enabled device may sense spectrum use in a region and, based on thedetected spectrum use, select one or more communication channels foruse. The devices also may detect one or more other DSA-enabled deviceswith which they can form DSA networks. A DSA network may monitorspectrum use by cooperative and non-cooperative devices, to dynamicallyselect one or more channels to use for communication while avoiding orreducing interference with other devices. A DSA network may includedetectors such as a narrow-band detector, wideband detector, TVdetector, radar detector, a wireless microphone detector, or anycombination thereof.

U.S. Pat. No. RE43,066 for “System and method for reuse ofcommunications spectrum for fixed and mobile applications with efficientmethod to mitigate interference” by inventor Mark Allen McHenry, filedDec. 2, 2008, describes a communications system network enablingsecondary use of spectrum on a non-interference basis. The system uses amodulation method to measure the background signals that eliminatesself-generated interference and also identifies the secondary signal toall primary users via on/off amplitude modulation, allowing easyresolution of interference claims. The system uses high-processing gainprobe waveforms that enable propagation measurements to be made withminimal interference to the primary users. The system measuresbackground signals and identifies the types of nearby receivers andmodifies the local frequency assignments to minimize interference causedby a secondary system due to non-linear mixing interference andinterference caused by out-of-band transmitted signals (phase noise,harmonics, and spurs). The system infers a secondary node's elevationand mobility (thus, its probability to cause interference) by analysisof the amplitude of background signals. Elevated or mobile nodes aregiven more conservative frequency assignments than stationary nodes.

U.S. Pat. No. 7,424,268 for “System and Method for Management of aShared Frequency Band” by inventors Diener, et al., filed Apr. 22, 2003,discloses a system, method, software and related functions for managingactivity in an unlicensed radio frequency band that is shared, both infrequency and time, by signals of multiple types. Signal pulse energy inthe band is detected and is used to classify signals according to signaltype. Using knowledge of the types of signals occurring in the frequencyband and other spectrum activity related statistics (referred to asspectrum intelligence), actions can be taken in a device or network ofdevices to avoid interfering with other signals, and in general tooptimize simultaneous use of the frequency band with the other signals.The spectrum intelligence may be used to suggest actions to a deviceuser or network administrator, or to automatically invoke actions in adevice or network of devices to maintain desirable performance.

U.S. Pat. No. 8,249,631 for “Transmission power allocation/controlmethod, communication device and program” by inventor Ryo Sawai, filedJul. 21, 2010, teaches a method for allocating transmission power to asecond communication service making secondary usage of a spectrumassigned to a first communication service, in a node which is able tocommunicate with a secondary usage node. The method determines aninterference power acceptable for two or more second communicationservices when the two or more second communication services are operatedand allocates the transmission powers to the two or more secondcommunication services.

U.S. Pat. No. 8,094,610 for “Dynamic cellular cognitive system” byinventors Wang, et al., filed Feb. 25, 2009, discloses permitting highquality communications among a diverse set of cognitive radio nodeswhile minimizing interference to primary and other secondary users byemploying dynamic spectrum access in a dynamic cellular cognitivesystem. Diverse device types interoperate, cooperate, and communicatewith high spectrum efficiency and do not require infrastructure to formthe network. The dynamic cellular cognitive system can expand to a widergeographical distribution via linking to existing infrastructure.

U.S. Pat. No. 8,565,811 for “Software-defined radio using multi-coreprocessor” by inventors Tan, et al., discloses a radio control boardpassing a plurality of digital samples between a memory of a computingdevice and a radio frequency (RF) transceiver coupled to a system bus ofthe computing device. Processing of the digital samples is carried outby one or more cores of a multi-core processor to implement asoftware-defined radio.

U.S. Pat. No. 8,064,840 for “Method and system for determining spectrumavailability within a network” by inventors McHenry, et al., filed Jun.18, 2009, discloses an invention which determines spectrum holes for acommunication network by accumulating the information obtained fromprevious received signals to determine the presence of a larger spectrumhole that allows a reduced listening period, higher transmit power and areduced probability of interference with other networks andtransmitters.

U.S. Publication No. 2009/0143019 for “Method and apparatus fordistributed spectrum sensing for wireless communication” by inventorStephen J. Shellhammer, filed Jan. 4, 2008, discloses methods andapparatus for determining if a licensed signal having or exceeding apredetermined field strength is present in a wireless spectrum. Thesignal of interest maybe a television signal or a wireless microphonesignal using licensed television spectrum.

U.S. Publication No. 2013/0090071 for “Systems and methods forcommunication in a white space” by inventors Abraham, et al., filed Apr.3, 2012, discloses systems, methods, and devices to communicate in awhite space. In some aspects, wireless communication transmitted in thewhite space authorizes an initial transmission by a device. The wirelesscommunication may include power information for determining a power atwhich to transmit the initial transmission. The initial transmission maybe used to request information identifying one or more channels in thewhite space available for transmitting data.

U.S. Publication No. 2012/0072986 for “Methods for detecting andclassifying signals transmitted over a radio frequency spectrum” byinventors Livsics, et al., filed Nov. 1, 2011, discloses a method toclassify a signal as non-cooperative (NC) or a target signal. Thepercentage of power above a first threshold is computed for a channel.Based on the percentage, a signal is classified as a narrowband signal.If the percentage indicates the absence of a narrowband signal, then alower second threshold is applied to confirm the absence according tothe percentage of power above the second threshold. The signal isclassified as a narrowband signal or pre-classified as a wideband signalbased on the percentage. Pre-classified wideband signals are classifiedas a wideband NC signal or target signal using spectrum masks.

U.S. Pat. No. 8,494,464 for “Cognitive networked electronic warfare” byinventors Kadambe, et al., filed Sep. 8, 2010, describes an apparatusfor sensing and classifying radio communications including sensor unitsconfigured to detect RF signals, a signal classifier configured toclassify the detected RF signals into a classification, theclassification including at least one known signal type and an unknownsignal type, a clustering learning algorithm capable of finding clustersof common signals among the previously seen unknown signals; it is thenfurther configured to use these clusters to retrain the signalclassifier to recognize these signals as a new signal type, aiming toprovide signal identification to better enable electronic attacks andjamming signals.

U.S. Publication No. 2011/0059747 for “Sensing Wireless TransmissionsFrom a Licensed User of a Licensed Spectral Resource” by inventorsLindoff, et al., filed Sep. 7, 2009, describes sensing wirelesstransmissions from a licensed user of a licensed spectral resourceincludes obtaining information indicating a number of adjacent sensorsthat are concurrently sensing wireless transmissions from the licenseduser of the licensed spectral resource. Such information can be obtainedfrom a main node controlling the sensor and its adjacent sensors, or bythe sensor itself (e.g., by means of short-range communication equipmenttargeting any such adjacent sensors). A sensing rate is then determinedas a function, at least in part, of the information indicating thenumber of adjacent sensors that are concurrently sensing wirelesstransmissions from the licensed user of the licensed spectral resource.Receiver equipment is then periodically operated at the determinedsensing rate, wherein the receiver equipment is configured to detectwireless transmissions from the licensed user of the licensed spectralresource.

U.S. Pat. No. 8,463,195 for “Methods and apparatus for spectrum sensingof signal features in a wireless channel” by inventor Shellhammer, filedNov. 13, 2009, discloses methods and apparatus for sensing features of asignal in a wireless communication system are disclosed. The disclosedmethods and apparatus sense signal features by determining a number ofspectral density estimates, where each estimate is derived based onreception of the signal by a respective antenna in a system withmultiple sensing antennas. The spectral density estimates are thencombined, and the signal features are sensed based on the combination ofthe spectral density estimates. Invention aims to increase sensingperformance by addressing problems associated with Rayleigh fading,which causes signals to be less detectable.

U.S. Pat. No. 8,151,311 for “System and method of detecting potentialvideo traffic interference” by inventors Huffman, et al., filed Nov. 30,2007, describes a method of detecting potential video trafficinterference at a video head-end of a video distribution network isdisclosed and includes detecting, at a video head-end, a signalpopulating an ultra-high frequency (UHF) white space frequency. Themethod also includes determining that a strength of the signal is equalto or greater than a threshold signal strength. Further, the methodincludes sending an alert from the video head-end to a networkmanagement system. The alert indicates that the UHF white spacefrequency is populated by a signal having a potential to interfere withvideo traffic delivered via the video head-end. Cognitive radiotechnology, various sensing mechanisms (energy sensing, NationalTelevision System Committee signal sensing, Advanced Television SystemsCommittee sensing), filtering, and signal reconstruction are disclosed.

U.S. Pat. No. 8,311,509 for “Detection, communication and control inmultimode cellular, TDMA, GSM, spread spectrum, CDMA, OFDM, WiLAN, andWiFi systems” by inventor Feher, filed Oct. 31, 2007, teaches a devicefor detection of signals, with location finder or location tracker ornavigation signal and with Modulation Demodulation (Modem) FormatSelectable (MFS) communication signal. Processor for processing adigital signal into cross-correlated in-phase and quadrature-phasefiltered signal and for processing a voice signal into OrthogonalFrequency Division Multiplexed (OFDM) or Orthogonal Frequency DivisionMultiple Access (OFDMA) signal. Each is used in a Wireless Local AreaNetwork (WLAN) and in Voice over Internet Protocol (VoIP) network.Device and location finder with Time Division Multiple Access (TDMA),Global Mobile System (GSM) and spread spectrum Code Division MultipleAccess (CDMA) is used in a cellular network. Polar and quadraturemodulator and two antenna transmitter for transmission of providedprocessed signal. Transmitter with two amplifiers operated in separateradio frequency (RF) bands. One transmitter is operated as aNon-Linearly Amplified (NLA) transmitter and the other transmitter isoperated as a linearly amplified or linearized amplifier transmitter.

U.S. Pat. No. 8,514,729 for “Method and system for analyzing RF signalsin order to detect and classify actively transmitting RF devices” byinventor Blackwell, filed Apr. 3, 2009, discloses methods andapparatuses to analyze RF signals in order to detect and classify RFdevices in wireless networks are described. The method includesdetecting one or more radio frequency (RF) samples; determining burstdata by identifying start and stop points of the one or more RF samples;comparing time domain values for an individual burst with time domainvalues of one or more predetermined RF device profiles; generating ahuman-readable result indicating whether the individual burst should beassigned to one of the predetermined RF device profiles; and,classifying the individual burst if assigned to one of the predeterminedRF device profiles as being a WiFi device or a non-WiFi device with thenon-WiFi device being a RF interference source to a wireless network.

SUMMARY OF THE INVENTION

The present invention relates to systems, methods and apparatus forautomatic signal detection with temporal feature extraction in an RFenvironment. An apparatus learns the RF environment in a predeterminedperiod based on statistical learning techniques, thereby creatinglearning data. A knowledge map is formed based on the learning data. Theapparatus automatically extracts temporal features of the RF environmentfrom the knowledge map. A real-time spectral sweep is scrubbed againstthe knowledge map. The apparatus is operable to detect a signal in theRF environment, which has a low power level or is a narrowband signalburied in a wideband signal, and which cannot be identified otherwise.

These and other aspects of the present invention will become apparent tothose skilled in the art after a reading of the following description ofthe preferred embodiment when considered with the drawings, as theysupport the claimed invention.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated herein and constitutepart of this specification, illustrate exemplary embodiments of theinvention, and together with the general description given above and thedetailed description given below, serve to explain the features of theinvention.

FIG. 1 is a system block diagram of a wireless environment suitable foruse with the various embodiments.

FIG. 2A is a block diagram of a spectrum management device according toan embodiment.

FIG. 2B is a schematic logic flow block diagram illustrating logicaloperations which may be performed by a spectrum management deviceaccording to an embodiment.

FIG. 3 is a process flow diagram illustrating an embodiment method foridentifying a signal.

FIG. 4 is a process flow diagram illustrating an embodiment method formeasuring sample blocks of a radio frequency scan.

FIGS. 5A-5C are a process flow diagram illustrating an embodiment methodfor determining signal parameters.

FIG. 6 is a process flow diagram illustrating an embodiment method fordisplaying signal identifications.

FIG. 7 is a process flow diagram illustrating an embodiment method fordisplaying one or more open frequency.

FIG. 8A is a block diagram of a spectrum management device according toanother embodiment.

FIG. 8B is a schematic logic flow block diagram illustrating logicaloperations which may be performed by a spectrum management deviceaccording to another embodiment.

FIG. 9 is a process flow diagram illustrating an embodiment method fordetermining protocol data and symbol timing data.

FIG. 10 is a process flow diagram illustrating an embodiment method forcalculating signal degradation data.

FIG. 11 is a process flow diagram illustrating an embodiment method fordisplaying signal and protocol identification information.

FIG. 12A is a block diagram of a spectrum management device according toa further embodiment.

FIG. 12B is a schematic logic flow block diagram illustrating logicaloperations which may be performed by a spectrum management deviceaccording to a further embodiment.

FIG. 13 is a process flow diagram illustrating an embodiment method forestimating a signal origin based on a frequency difference of arrival.

FIG. 14 is a process flow diagram illustrating an embodiment method fordisplaying an indication of an identified data type within a signal.

FIG. 15 is a process flow diagram illustrating an embodiment method fordetermining modulation type, protocol data, and symbol timing data.

FIG. 16 is a process flow diagram illustrating an embodiment method fortracking a signal origin.

FIG. 17 is a schematic diagram illustrating an embodiment for scanningand finding open space.

FIG. 18 is a diagram of an embodiment wherein software defined radionodes are in communication with a master transmitter and device sensingmaster.

FIG. 19 is a process flow diagram of an embodiment method of temporallydividing up data into intervals for power usage analysis.

FIG. 20 is a flow diagram illustrating an embodiment wherein frequencyto license matching occurs.

FIG. 21 is a flow diagram illustrating an embodiment method forreporting power usage information.

FIG. 22 is a flow diagram illustrating an embodiment method for creatingfrequency arrays.

FIG. 23 is a flow diagram illustrating an embodiment method for reframeand aggregating power when producing frequency arrays.

FIG. 24 is a flow diagram illustrating an embodiment method of reportinglicense expirations.

FIG. 25 is a flow diagram illustrating an embodiment method of reportingfrequency power use.

FIG. 26 is a flow diagram illustrating an embodiment method ofconnecting devices.

FIG. 27 is a flow diagram illustrating an embodiment method ofaddressing collisions.

FIG. 28 is a schematic diagram of an embodiment of the inventionillustrating a virtualized computing network and a plurality ofdistributed devices.

FIG. 29 is a schematic diagram of an embodiment of the presentinvention.

FIG. 30 is a schematic diagram illustrating the present invention in avirtualized or cloud computing system with a network and a mobilecomputer or mobile communications device.

FIGS. 31-34 show screen shot illustrations for automatic signaldetection indications on displays associated with the present invention.

FIG. 35 is an example of a receiver that has marked variations onbaseline behavior across a wide spectrum (9 MHz-6 GHz).

FIG. 36 shows a normal spectrum from 700 MHz to 790 MHz in oneembodiment.

FIG. 37 shows the same spectrum as in FIG. 36 at a different time.

FIG. 38 illustrates a spectrum from 1.9 GHz to 2.0 GHz, along with someadditional lines that indicate the functions of the new algorithm.

FIG. 39 is a close up view of the first part of the overall spectrum inFIG. 38.

FIG. 40 illustrates a knowledge map obtained by a TFE process.

FIG. 41 illustrates an interpretation operation based on a knowledgemap.

FIG. 42 shows the identification of signals, which are represented bythe black brackets above the knowledge display.

FIG. 43 shows more details of the narrow band signals at the left of thespectrum around 400 MHz in FIG. 42.

FIG. 44 shows more details of the wide band signals and narrow bandsignals between 735 MHz and 790 MHz in FIG. 42.

FIG. 45 illustrates an operation of the ASD in the present invention.

FIG. 46 provides a flow diagram for geolocation in the presentinvention.

FIG. 47 illustrates a configuration of a PDFT processor according to oneembodiment of the present invention.

FIG. 48 is a flow chart for data processing in a PDFT processoraccording to one embodiment of the present invention.

FIG. 49 illustrates data analytics in an analyzer engine according toone embodiment of the present invention.

FIG. 50 illustrates a mask according to one embodiment of the presentinvention.

FIG. 51 illustrates a workflow of automatic signal detection accordingto one embodiment of the present invention.

FIG. 52 is a screenshot illustrating alarm visualization via a graphicaluser interface according to one embodiment of the present invention.

FIG. 53 illustrates a comparison of live FFT stream data and a maskconsidering a db offset according to one embodiment of the presentinvention.

FIG. 54 is a snippet of the code of the detection algorithm defining aflag according to one embodiment of the embodiment.

FIG. 55 is a snippet of the code of the detection algorithm identifyingpeak values according to one embodiment of the present invention.

FIG. 56 illustrates a complex spectrum situation according to oneembodiment of the present invention.

FIG. 57 is an analysis of the live stream data above the mask in thefirst alarm duration in FIG. 56.

FIG. 58 is a snippet of the code of the detection algorithm checking thealarm duration according to one embodiment of the present invention.

FIG. 59 is a snippet of the code of the detection algorithm triggeringan alarm according to one embodiment of the present invention.

FIG. 60 is a screenshot illustrating a job manager screen according toone embodiment of the present invention.

FIG. 61 illustrates trigger and alarm management according to oneembodiment of the present invention.

FIG. 62 is a screenshot illustrating a spectrum with RF signals andrelated analysis.

FIG. 63 is a screenshot illustrating identified signals based on theanalysis in FIG. 16.

FIG. 64 is a diagram of a modular architecture according to oneembodiment of the present invention.

FIG. 65 illustrates a communications environment according to oneembodiment of the present invention.

FIG. 66 illustrates an UAS interface according to one embodiment of thepresent invention.

FIG. 67 lists signal strength measurements according to one embodimentof the present invention.

FIG. 68 illustrates a focused jammer in a mobile application accordingto one embodiment of the present invention.

FIG. 69 illustrates a swept RF interference by a jammer according to oneembodiment of the present invention.

FIG. 70 illustrates data collection, distillation and reportingaccording to one embodiment of the present invention.

FIG. 71 is a comparison of multiple methodologies for detecting andclassifying UAS.

FIG. 72 lists capabilities of an RF-based counter-UAS system accordingto one embodiment of the present invention.

FIG. 73 illustrates an RF-based counter-UAS system deployed as along-distance detection model according to one embodiment of the presentinvention.

FIG. 74 illustrates features of drones in the OcuSync family.

FIG. 75 illustrates features of drones in the Lightbridge family.

FIG. 76 illustrates a spectrum monitoring system detecting an anomaloussignal in close proximity of critical infrastructure.

FIG. 77 illustrates a system configuration and interface according toone embodiment of the present invention.

FIG. 78 is a screenshot illustrating no alarm going off for an anomaloussignal from LMR traffic not in proximity of the site according to oneembodiment of the present invention.

FIG. 79 illustrates a GUI of a remote alarm manager according to oneembodiment of the present invention.

FIG. 80 labels different parts of a front panel of a spectrum monitoringdevice according to one embodiment of the present invention.

FIG. 81 lists all the labels in FIG. 79 representing different part ofthe front panel of the spectrum monitoring device according to oneembodiment of the present invention.

FIG. 82 illustrates a spectrum monitoring device scanning a spectrumfrom 40 MHz to 6 GHz according to one embodiment of the presentinvention.

FIG. 83 lists the capabilities of a spectrum monitoring system accordingto 5 main on-network mobile phone states plus 1 no-network mobile phonestate.

FIG. 84 illustrates a mobile event analysis per one minute intervalsaccording to one embodiment of the present invention.

FIG. 85 is a site cellular survey result according to one embodiment ofthe present invention.

FIG. 86 illustrates a system of a spectrum management node device innetwork communication with a video sensor according to one embodiment ofthe present invention.

FIG. 87 is a diagram of a cluster layout for multiple systemsillustrated in FIG. 86 according to one embodiment of the presentinvention.

DETAILED DESCRIPTION

Related US patents and patent applications include U.S. application Ser.No. 16/275,575, U.S. application Ser. No. 16/180,690, U.S. Pat. No.10,244,504, U.S. application Ser. No. 16/360,841, U.S. application Ser.No. 15/357,157, U.S. Pat. Nos. 9,537,586, 9,185,591, 8,977,212,8,798,548, 8,805,291, 8,780,968, 8,824,536, 9,288,683, 9,078,162, U.S.application Ser. No. 13/913,013, and U.S. Application No. 61/789,758.Each of these patent documents is incorporated herein by reference intheir entirety.

The present invention addresses the longstanding, unmet needs existingin the prior art and commercial sectors to provide solutions to the atleast four major problems existing before the present invention, eachone that requires near real time results on a continuous scanning of thetarget environment for the spectrum.

The present invention relates to systems, methods, and devices of thevarious embodiments enable spectrum management by identifying,classifying, and cataloging signals of interest based on radio frequencymeasurements. Furthermore, present invention relates to spectrumanalysis and management for radio frequency (RF) signals, and forautomatically identifying baseline data and changes in state for signalsfrom a multiplicity of devices in a wireless communications spectrum,and for providing remote access to measured and analyzed data through avirtualized computing network. In an embodiment, signals and theparameters of the signals may be identified and indications of availablefrequencies may be presented to a user. In another embodiment, theprotocols of signals may also be identified. In a further embodiment,the modulation of signals, data types carried by the signals, andestimated signal origins may be identified.

It is an object of this invention to provide an apparatus foridentifying signal emitting devices including: a housing, at least oneprocessor and memory, at least one receiver and sensors constructed andconfigured for sensing and measuring wireless communications signalsfrom signal emitting devices in a spectrum associated with wirelesscommunications; and wherein the apparatus is operable to automaticallyanalyze the measured data to identify at least one signal emittingdevice in near real time from attempted detection and identification ofthe at least one signal emitting device, and then to identify open spaceavailable for wireless communications, based upon the information aboutthe signal emitting device(s) operating in the predetermined spectrum;furthermore, the present invention provides baseline data and changes instate for compressed data to enable near real time analytics and resultsfor individual units and for aggregated units for making uniquecomparisons of data.

The present invention further provides systems for identifying whitespace in wireless communications spectrum by detecting and analyzingsignals from any signal emitting devices including at least oneapparatus, wherein the at least one apparatus is operable fornetwork-based communication with at least one server computer includinga database, and/or with at least one other apparatus, but does notrequire a connection to the at least one server computer to be operablefor identifying signal emitting devices; wherein each of the apparatusis operable for identifying signal emitting devices including: ahousing, at least one processor and memory, at least one receiver, andsensors constructed and configured for sensing and/or measuring wirelesscommunications signals from signal emitting devices in a spectrum forwireless communications; and wherein the apparatus is operable toautomatically analyze the measured data to identify at least one signalemitting device in near real time from attempted detection andidentification of the at least one signal emitting device, and then toidentify open space available for wireless communications, based uponthe information about the signal emitting device(s) operating in thepredetermined spectrum; all of the foregoing using baseline data andchanges in state for compressed data to enable near real time analyticsand results for individual units and for aggregated units for makingunique comparisons of data.

The present invention is further directed to a method for identifyingbaseline data and changes in state for compressed data to enable nearreal time analytics and results for individual units and for aggregatedunits and storing the aggregated data in a database and providingsecure, remote access to the compressed data for each unit and to theaggregated data via network-based virtualized computing system orcloud-based system, for making unique comparisons of data in a wirelesscommunications spectrum including the steps of: providing a device formeasuring characteristics of signals from signal emitting devices in aspectrum associated with wireless communications, with measured datacharacteristics including frequency, power, bandwidth, duration,modulation, and combinations thereof; the device including a housing, atleast one processor and memory, and sensors constructed and configuredfor sensing and measuring wireless communications signals within thespectrum; and further including the following steps performed within thedevice housing: assessing whether the measured data includes analogand/or digital signal(s); determining a best fit based on frequency, ifthe measured power spectrum is designated in an historical or areference database(s) for frequency ranges; automatically determining acategory for either analog or digital signals, based on power andsideband combined with frequency allocation; determining a TDM/FDM/CDMsignal, based on duration and bandwidth; identifying at least one signalemitting device from the composite results of the foregoing steps; andthen automatically identifying the open space available for wirelesscommunications, based upon the information about the signal emittingdevice(s) operating in the predetermined spectrum; all using baselinedata and changes in state for compressed data to enable near real timeanalytics and results for individual units and for aggregated units formaking unique comparisons of data.

Additionally, the present invention provides systems, apparatus, andmethods for identifying open space in a wireless communications spectrumusing an apparatus having a multiplicity of processors and memory, atleast one receiver, sensors, and communications transmitters andreceivers, all constructed and configured within a housing for automatedanalysis of detected signals from signal emitting devices, determinationof signal duration and other signal characteristics, and automaticallygenerating information relating to device identification, open space,signal optimization, all using baseline data and changes in state forcompressed data to enable near real time analytics and results forindividual units and for aggregated units for making unique comparisonsof data within the spectrum for wireless communication, and forproviding secure, remote access via a network to the data stored in avirtualized computer system.

Referring now to the drawings in general, the illustrations are for thepurpose of describing at least one preferred embodiment and/or examplesof the invention and are not intended to limit the invention thereto.Various embodiments are described in detail with reference to theaccompanying drawings. Wherever possible, the same reference numbers areused throughout the drawings to refer to the same or like parts.References made to particular examples and implementations are forillustrative purposes, and are not intended to limit the scope of theinvention or the claims.

The word “exemplary” is used herein to mean “serving as an example,instance, or illustration.” Any implementation described herein as“exemplary” is not necessarily to be construed as preferred oradvantageous over other implementations.

The present invention provides systems, methods, and devices forspectrum analysis and management by identifying, classifying, andcataloging at least one or a multiplicity of signals of interest basedon radio frequency measurements and location and other measurements, andusing near real-time parallel processing of signals and theircorresponding parameters and characteristics in the context ofhistorical and static data for a given spectrum, and more particularly,all using baseline data and changes in state for compressed data toenable near real time analytics and results for individual units and foraggregated units for making unique comparisons of data.

The systems, methods and apparatus according to the present inventionpreferably have the ability to detect in near real time, and morepreferably to detect, sense, measure, and/or analyze in near real time,and more preferably to perform any near real time operations withinabout 1 second or less. Advantageously, the present invention and itsreal time functionality described herein uniquely provide and enable theapparatus units to compare to historical data, to update data and/orinformation, and/or to provide more data and/or information on the openspace, on the apparatus unit or device that may be occupying the openspace, and combinations, in the near real time compared with thehistorically scanned (15 min to 30 days) data, or historical databaseinformation. Also, the data from each apparatus unit or device and/orfor aggregated data from more than one apparatus unit or device arecommunicated via a network to at least one server computer and stored ona database in a virtualized or cloud-based computing system, and thedata is available for secure, remote access via the network fromdistributed remote devices having software applications (apps) operablethereon, for example by web access (mobile app) or computer access(desktop app).

The systems, methods, and devices of the various embodiments enablespectrum management by identifying, classifying, and cataloging signalsof interest based on radio frequency measurements. In an embodiment,signals and the parameters of the signals may be identified andindications of available frequencies may be presented to a user. Inanother embodiment, the protocols of signals may also be identified. Ina further embodiment, the modulation of signals, data types carried bythe signals, and estimated signal origins may be identified.

Embodiments are directed to a spectrum management device that may beconfigurable to obtain spectrum data over a wide range of wirelesscommunication protocols. Embodiments may also provide for the ability toacquire data from and sending data to database depositories that may beused by a plurality of spectrum management customers.

In one embodiment, a spectrum management device may include a signalspectrum analyzer that may be coupled with a database system andspectrum management interface. The device may be portable or may be astationary installation and may be updated with data to allow the deviceto manage different spectrum information based on frequency, bandwidth,signal power, time, and location of signal propagation, as well asmodulation type and format and to provide signal identification,classification, and geo-location. A processor may enable the device toprocess spectrum power density data as received and to process raw I/Qcomplex data that may be used for further signal processing, signalidentification, and data extraction.

In an embodiment, a spectrum management device or apparatus unit maycomprise a low noise amplifier that receives a radio frequency (RF)energy from an antenna. The antenna may be any antenna structure that iscapable of receiving RF energy in a spectrum of interest. The low noiseamplifier may filter and amplify the RF energy. The RF energy may beprovided to an RF translator. The RF translator may perform a fastFourier transform (FFT) and either a square magnitude or a fastconvolution spectral periodogram function to convert the RF measurementsinto a spectral representation. In an embodiment, the RF translator mayalso store a timestamp to facilitate calculation of a time of arrivaland an angle of arrival. The In-Phase and Quadrature (I/Q) data may beprovided to a spectral analysis receiver or it may be provided to asample data store where it may be stored without being processed by aspectral analysis receiver. The input RF energy may also be directlydigital down-converted and sampled by an analog to digital converter(ADC) to generate complex I/Q data. The complex I/Q data may beequalized to remove multipath, fading, white noise and interference fromother signaling systems by fast parallel adaptive filter processes. Thisdata may then be used to calculate modulation type and baud rate.Complex sampled I/Q data may also be used to measure the signal angle ofarrival and time of arrival. Such information as angle of arrival andtime of arrival may be used to compute more complex and precisedirection finding. In addition, they may be used to apply geo-locationtechniques. Data may be collected from known signals or unknown signalsand time spaced in order to provide expedient information. I/Q sampleddata may contain raw signal data that may be used to demodulate andtranslate signals by streaming them to a signal analyzer or to areal-time demodulator software defined radio that may have the newlyidentified signal parameters for the signal of interest. The inherentnature of the input RF allows for any type of signal to be analyzed anddemodulated based on the reconfiguration of the software defined radiointerfaces.

A spectral analysis receiver may be configured to read raw In-Phase (I)and Quadrature (Q) data and either translate directly to spectral dataor down convert to an intermediate frequency (IF) up to half the Nyquistsampling rate to analyze the incoming bandwidth of a signal. Thetranslated spectral data may include sensed data and/or measured valuesof signal energy, frequency, and/or time. The sensed data and/ormeasured values provide attributes of at least one signal. For at leastone signal under review, the sensed data and/or measured values provideattributes of at least one signal associated with at least one device.Furthermore, these attributes of at least one signal are processed forcomparison with at least one historical dataset and/or at least oneother dataset associated with the spectrum to confirm the detection of aparticular signal of interest within a spectrum of interest. In oneembodiment, machine learning and/or artificial intelligence (AI)algorithms are used for automatic processing for comparison with the atleast one other dataset. In an embodiment, a spectral analysis receiverhas a referenced spectrum input range between 0 Hz to 12.4 GHz,preferably not lower than 9 kHz, with capability of fiber optic inputfor spectrum input up to 60 GHz.

For each device, at least one receiver is used. In one embodiment, thespectral analysis receiver may be configured to sample the input RF databy fast analog down-conversion of the RF signal. The down-convertedsignal may then be digitally converted and processed by fast convolutionfilters to obtain a power spectrum. This process may also providespectrum measurements including the signal power, the bandwidth, thecenter frequency of the signal as well as a Time of Arrival (TOA)measurement. The TOA measurement may be used to create a timestamp ofthe detected signal and/or to generate a time difference of arrivaliterative process for direction finding and fast triangulation ofsignals. In an embodiment, the sample data may be provided to a spectrumanalysis module. In an embodiment, the spectrum analysis module mayevaluate the sample data to obtain the spectral components of thesignal.

In an embodiment, the spectral components of the signal may be obtainedby the spectrum analysis module from the raw I/Q data as provided by anRF translator. The I/Q data analysis performed by the spectrum analysismodule may operate to extract more detailed information about thesignal, including by way of example, modulation type (e.g., FM, AM,QPSK, 16QAM, etc.) and/or protocol (e.g., GSM, CDMA, OFDM, LTE, etc.).In an embodiment, the spectrum analysis module may be configured by auser to obtain specific information about a signal of interest. In analternate embodiment, the spectral components of the signal may beobtained from power spectral component data produced by the spectralanalysis receiver.

In an embodiment, the spectrum analysis module may provide the spectralcomponents of the signal to a data extraction module. The dataextraction module may provide the classification and categorization ofsignals detected in the RF spectrum. The data extraction module may alsoacquire additional information regarding the signal from the spectralcomponents of the signal. For example, the data extraction module mayprovide modulation type, bandwidth, and possible system in useinformation. In another embodiment, the data extraction module mayselect and organize the extracted spectral components in a formatselected by a user.

The information from the data extraction module may be provided to aspectrum management module. The spectrum management module may generatea query to a static database to classify a signal based on itscomponents. For example, the information stored in static database maybe used to determine the spectral density, center frequency, bandwidth,baud rate, modulation type, protocol (e.g., GSM, CDMA, OFDM, LTE, etc.),system or carrier using licensed spectrum, location of the signalsource, and a timestamp of the signal of interest. These data points maybe provided to a data store for export. In an embodiment and as morefully described below, the data store may be configured to accessmapping software to provide the user with information on the location ofthe transmission source of the signal of interest. In an embodiment, thestatic database includes frequency information gathered from varioussources including, but not limited to, the Federal CommunicationCommission, the International Telecommunication Union, and data fromusers. As an example, the static database may be an SQL database. Thedata store may be updated, downloaded or merged with other devices orwith its main relational database. Software API applications may beincluded to allow database merging with third-party spectrum databasesthat may only be accessed securely.

In the various embodiments, the spectrum management device may beconfigured in different ways. In an embodiment, the front end of thesystem may comprise various hardware receivers that may provide In-Phaseand Quadrature complex data. The front end receiver may include API setcommands via which the system software may be configured to interface(i.e., communicate) with a third party receiver. In an embodiment, thefront end receiver may perform the spectral computations using FFT (FastFourier Transform) and other DSP (Digital Signal Processing) to generatea fast convolution periodogram that may be re-sampled and averaged toquickly compute the spectral density of the RF environment.

In an embodiment, cyclic processes may be used to average and correlatesignal information by extracting the changes inside the signal to betteridentify the signal of interest that is present in the RF space. Acombination of amplitude and frequency changes may be measured andaveraged over the bandwidth time to compute the modulation type andother internal changes, such as changes in frequency offsets, orthogonalfrequency division modulation, changes in time (e.g., Time DivisionMultiplexing), and/or changes in I/Q phase rotation used to compute thebaud rate and the modulation type. In an embodiment, the spectrummanagement device may have the ability to compute several processes inparallel by use of a multi-core processor and along with severalembedded field programmable gate arrays (FPGA). Such multi-coreprocessing may allow the system to quickly analyze several signalparameters in the RF environment at one time in order to reduce theamount of time it takes to process the signals. The amount of signalscomputed at once may be determined by their bandwidth requirements.Thus, the capability of the system may be based on a maximum frequencyFs/2. The number of signals to be processed may be allocated based ontheir respective bandwidths. In another embodiment, the signal spectrummay be measured to determine its power density, center frequency,bandwidth and location from which the signal is emanating and a bestmatch may be determined based on the signal parameters based oninformation criteria of the frequency.

In another embodiment, a GPS and direction finding location (DF) systemmay be incorporated into the spectrum management device and/or availableto the spectrum management device. Adding GPS and DF ability may enablethe user to provide a location vector using the National MarineElectronics Association's (NMEA) standard form. In an embodiment,location functionality is incorporated into a specific type of GPS unit,such as a U.S. government issued receiver. The information may bederived from the location presented by the database internal to thedevice, a database imported into the device, or by the user inputtinggeo-location parameters of longitude and latitude which may be derivedas degrees, minutes and seconds, decimal minutes, or decimal form andtranslated to the necessary format with the default being ‘decimal’form. This functionality may be incorporated into a GPS unit. The signalinformation and the signal classification may then be used to locate thesignaling device as well as to provide a direction finding capability.

A type of triangulation using three units as a group antennaconfiguration performs direction finding by using multilateration.Commonly used in civil and military surveillance applications,multilateration is able to accurately locate an aircraft, vehicle, orstationary emitter by measuring the “Time Difference of Arrival” (TDOA)of a signal from the emitter at three or more receiver sites. If a pulseis emitted from a platform, it will arrive at slightly different timesat two spatially separated receiver sites, the TDOA being due to thedifferent distances of each receiver from the platform. This locationinformation may then be supplied to a mapping process that utilizes adatabase of mapping images that are extracted from the database based onthe latitude and longitude provided by the geo-location or directionfinding device. The mapping images may be scanned in to show the pointsof interest where a signal is either expected to be emanating from basedon the database information or from an average taken from the databaseinformation and the geo-location calculation performed prior to themapping software being called. The user can control the map to maximizeor minimize the mapping screen to get a better view which is more fit toprovide information of the signal transmissions. In an embodiment, themapping process does not rely on outside mapping software. The mappingcapability has the ability to generate the map image and to populate amapping database that may include information from third party maps tomeet specific user requirements.

In an embodiment, triangulation and multilateration may utilize aBayesian type filter that may predict possible movement and futurelocation and operation of devices based on input collected from the TDOAand geolocation processes and the variables from the static databasepertaining to the specified signal of interest. The Bayesian filtertakes the input changes in time difference and its inverse function(i.e., frequency difference) and takes an average change in signalvariation to detect and predict the movement of the signals. The signalchanges are measured within 1 ns time difference and the filter may alsoadapt its gradient error calculation to remove unwanted signals that maycause errors due to signal multipath, inter-symbol interference, andother signal noise.

In an embodiment the changes within a 1 ns time difference for eachsample for each unique signal may be recorded. The spectrum managementdevice may then perform the inverse and compute and record the frequencydifference and phase difference between each sample for each uniquesignal. The spectrum management device may take the same signal andcalculates an error based on other input signals coming in within the 1ns time and may average and filter out the computed error to equalizethe signal. The spectrum management device may determine the timedifference and frequency difference of arrival for that signal andcompute the odds of where the signal is emanating from based on thefrequency band parameters presented from the spectral analysis andprocessor computations, and determines the best position from which thesignal is transmitted (i.e., origin of the signal).

FIG. 1 illustrates a wireless environment 100 suitable for use with thevarious embodiments. The wireless environment 100 may include varioussources 104, 106, 108, 110, 112, and 114 generating various radiofrequency (RF) signals 116, 118, 120, 122, 124, 126. As an example,mobile devices 104 may generate cellular RF signals 116, such as CDMA,GSM, 3G signals, etc. As another example, wireless access devices 106,such as Wi-Fi® routers, may generate RF signals 118, such as Wi-Fi®signals. As a further example, satellites 108, such as communicationsatellites or GPS satellites, may generate RF signals 120, such assatellite radio, television, or GPS signals. As a still further example,base stations 110, such as a cellular base station, may generate RFsignals 122, such as CDMA, GSM, 3G signals, etc. As another example,radio towers 112, such as local AM or FM radio stations, may generate RFsignals 124, such as AM or FM radio signals. As another example,government service provides 114, such as police units, fire fighters,military units, air traffic control towers, etc. may generate RF signals126, such as radio communications, tracking signals, etc. The various RFsignals 116, 118, 120, 122, 124, 126 may be generated at differentfrequencies, power levels, in different protocols, with differentmodulations, and at different times. The various sources 104, 106, 108,110, 112, and 114 may be assigned frequency bands, power limitations, orother restrictions, requirements, and/or licenses by a governmentspectrum control entity, such as the FCC. However, with so manydifferent sources 104, 106, 108, 110, 112, and 114 generating so manydifferent RF signals 116, 118, 120, 122, 124, 126, overlaps,interference, and/or other problems may occur. A spectrum managementdevice 102 in the wireless environment 100 may measure the RF energy inthe wireless environment 100 across a wide spectrum and identify thedifferent RF signals 116, 118, 120, 122, 124, 126 which may be presentin the wireless environment 100. The identification and cataloging ofthe different RF signals 116, 118, 120, 122, 124, 126 which may bepresent in the wireless environment 100 may enable the spectrummanagement device 102 to determine available frequencies for use in thewireless environment 100. In addition, the spectrum management device102 may be able to determine if there are available frequencies for usein the wireless environment 100 under certain conditions (i.e., day ofweek, time of day, power level, frequency band, etc.). In this manner,the RF spectrum in the wireless environment 100 may be managed.

FIG. 2A is a block diagram of a spectrum management device 202 accordingto an embodiment. The spectrum management device 202 may include anantenna structure 204 configured to receive RF energy expressed in awireless environment. The antenna structure 204 may be any type antenna,and may be configured to optimize the receipt of RF energy across a widefrequency spectrum. The antenna structure 204 may be connected to one ormore optional amplifiers and/or filters 208 which may boost, smooth,and/or filter the RF energy received by antenna structure 204 before theRF energy is passed to an RF receiver 210 connected to the antennastructure 204. In an embodiment, the RF receiver 210 may be configuredto measure the RF energy received from the antenna structure 204 and/oroptional amplifiers and/or filters 208. In an embodiment, the RFreceiver 210 may be configured to measure RF energy in the time domainand may convert the RF energy measurements to the frequency domain. Inan embodiment, the RF receiver 210 may be configured to generatespectral representation data of the received RF energy. The RF receiver210 is selected from any type RF receiver, and is configured to generateRF energy measurements over a range of frequencies. In one embodiment,the RF receiver is operable to measure and/or monitor a range offrequencies between 0 kHz to 24 GHz. In a preferred embodiment, the RFreceiver is operable to measure and/or monitor a range of frequenciesbetween 9 kHz to 6 GHz. In an embodiment, the frequency scanned by theRF receiver 210 may be user selectable. In an embodiment, the RFreceiver 210 may be connected to a signal processor 214 and may beconfigured to output RF energy measurements to the signal processor 214.As an example, the RF receiver 210 may output raw In-Phase (I) andQuadrature (Q) data to the signal processor 214. As another example, theRF receiver 210 may apply signals processing techniques to outputcomplex In-Phase (I) and Quadrature (Q) data to the signal processor214. In an embodiment, the spectrum management device may also includean antenna 206 connected to a location receiver 212, such as a GPSreceiver, which may be connected to the signal processor 214. Thelocation receiver 212 may provide location inputs to the signalprocessor 214.

The signal processor 214 may include a signal detection module 216, acomparison module 222, a timing module 224, and a location module 225.Additionally, the signal processor 214 may include an optional memorymodule 226 which may include one or more optional buffers 228 forstoring data generated by the other modules of the signal processor 214.

In an embodiment, the signal detection module 216 may operate toidentify signals based on the RF energy measurements received from theRF receiver 210. The signal detection module 216 may include a FastFourier Transform (FFT) module 217 which may convert the received RFenergy measurements into spectral representation data. The signaldetection module 216 may include an analysis module 221 which mayanalyze the spectral representation data to identify one or more signalsabove a power threshold.

A power module 220 of the signal detection module 216 may control thepower threshold at which signals may be identified. In an embodiment,the power threshold may be a default power setting or may be a userselectable power setting. A noise module 219 of the signal detectionmodule 216 may control a signal threshold, such as a noise threshold, ator above which signals may be identified.

The signal detection module 216 may include a parameter module 218 whichmay determine one or more signal parameters for any identified signals,such as center frequency, bandwidth, power, number of detected signals,frequency peak, peak power, average power, signal duration, etc. In anembodiment, the signal processor 214 may include a timing module 224which may record time information and provide the time information tothe signal detection module 216. Additionally, the signal processor 214may include a location module 225 which may receive location inputs fromthe location receiver 212 and determine a location of the spectrummanagement device 202. The location of the spectrum management device202 may be provided to the signal detection module 216.

In an embodiment, the signal processor 214 may be connected to one ormore memory 230. The memory 230 may include multiple databases, such asa history or historical database 232 and characteristics listing 236,and one or more buffers 240 storing data generated by signal processor214. While illustrated as connected to the signal processor 214 thememory 230 may also be on chip memory residing on the signal processor214 itself. In an embodiment, the history or historical database 232 mayinclude measured signal data 234 for signals that have been previouslyidentified by the spectrum management device 202. The measured signaldata 234 may include the raw RF energy measurements, time stamps,location information, one or more signal parameters for any identifiedsignals, such as center frequency, bandwidth, power, number of detectedsignals, frequency peak, peak power, average power, signal duration,etc., and identifying information determined from the characteristicslisting 236. In an embodiment, the history or historical database 232may be updated as signals are identified by the spectrum managementdevice 202. In an embodiment, the characteristic listing 236 may be adatabase of static signal data 238. The static signal data 238 mayinclude data gathered from various sources including by way of exampleand not by way of limitation the Federal Communication Commission, theInternational Telecommunication Union, telecom providers, manufacturedata, and data from spectrum management device users. Static signal data238 may include known signal parameters of transmitting devices, such ascenter frequency, bandwidth, power, number of detected signals,frequency peak, peak power, average power, signal duration, geographicinformation for transmitting devices, and any other data that may beuseful in identifying a signal. In an embodiment, the static signal data238 and the characteristic listing 236 may correlate signal parametersand signal identifications. As an example, the static signal data 238and characteristic listing 236 may list the parameters of the local fireand emergency communication channel correlated with a signalidentification indicating that signal is the local fire and emergencycommunication channel.

In an embodiment, the signal processor 214 may include a comparisonmodule 222 which may match data generated by the signal detection module216 with data in the history or historical database 232 and/orcharacteristic listing 236. In an embodiment the comparison module 222may receive signal parameters from the signal detection module 216, suchas center frequency, bandwidth, power, number of detected signals,frequency peak, peak power, average power, signal duration, and/orreceive parameter from the timing module 224 and/or location module 225.The parameter match module 223 may retrieve data from the history orhistorical database 232 and/or the characteristic listing 236 andcompare the retrieved data to any received parameters to identifymatches. Based on the matches the comparison module may identify thesignal. In an embodiment, the signal processor 214 may be optionallyconnected to a display 242, an input device 244, and/or networktransceiver 246. The display 242 may be controlled by the signalprocessor 214 to output spectral representations of received signals,signal characteristic information, and/or indications of signalidentifications on the display 242. In an embodiment, the input device244 may be any input device, such as a keyboard and/or knob, mouse,virtual keyboard or even voice recognition, enabling the user of thespectrum management device 202 to input information for use by thesignal processor 214. In an embodiment, the network transceiver 246 mayenable the spectrum management device 202 to exchange data with wiredand/or wireless networks, such as to update the characteristic listing236 and/or upload information from the history or historical database232.

FIG. 2B is a schematic logic flow block diagram illustrating logicaloperations which may be performed by a spectrum management device 202according to an embodiment. A receiver 210 may output RF energymeasurements, such as I and Q data to a FFT module 252 which maygenerate a spectral representation of the RF energy measurements whichmay be output on a display 242. The I and Q data may also be buffered ina buffer 256 and sent to a signal detection module 216. The signaldetection module 216 may receive location inputs from a locationreceiver 212 and use the received I and Q data to detect signals. Datafrom the signal detection module 216 may be buffered in a buffer 262 andwritten into a history or historical database 232. Additionally, datafrom the historical database may be used to aid in the detection ofsignals by the signal detection module 216. The signal parameters of thedetected signals may be determined by a signal parameters module 218using information from the history or historical database 232 and/or astatic database 238 listing signal characteristics through a buffer 268.Data from the signal parameters module 218 may be stored in the historyor historical database 232 and/or sent to the signal detection module216 and/or display 242. In this manner, signals may be detected andindications of the signal identification may be displayed to a user ofthe spectrum management device.

FIG. 3 illustrates a process flow of an embodiment method 300 foridentifying a signal. In an embodiment the operations of method 300 maybe performed by the processor 214 of a spectrum management device 202.In block 302 the processor 214 may determine the location of thespectrum management device 202. In an embodiment, the processor 214 maydetermine the location of the spectrum management device 202 based on alocation input, such as GPS coordinates, received from a locationreceiver, such as a GPS receiver 212. In block 304 the processor 214 maydetermine the time. As an example, the time may be the current clocktime as determined by the processor 214 and may be a time associatedwith receiving RF measurements. In block 306 the processor 214 mayreceive RF energy measurements. In an embodiment, the processor 214 mayreceive RF energy measurements from an RF receiver 210. In block 308 theprocessor 214 may convert the RF energy measurements to spectralrepresentation data. As an example, the processor may apply a FastFourier Transform (FFT) to the RF energy measurements to convert them tospectral representation data. In optional block 310 the processor 214may display the spectral representation data on a display 242 of thespectrum management device 202, such as in a graph illustratingamplitudes across a frequency spectrum.

In block 312 the processor 214 may identify one or more signal above athreshold. In an embodiment, the processor 214 may analyze the spectralrepresentation data to identify a signal above a power threshold. Apower threshold may be an amplitude measure selected to distinguish RFenergies associated with actual signals from noise. In an embodiment,the power threshold may be a default value. In another embodiment, thepower threshold may be a user selectable value. In block 314 theprocessor 214 may determine signal parameters of any identified signalor signals of interest. As examples, the processor 214 may determinesignal parameters such as center frequency, bandwidth, power, number ofdetected signals, frequency peak, peak power, average power, signalduration for the identified signals. In block 316 the processor 214 maystore the signal parameters of each identified signal, a locationindication, and time indication for each identified signal in a historydatabase 232. In an embodiment, a history database 232 may be a databaseresident in a memory 230 of the spectrum management device 202 which mayinclude data associated with signals actually identified by the spectrummanagement device.

In block 318 the processor 214 may compare the signal parameters of eachidentified signal to signal parameters in a signal characteristiclisting. In an embodiment, the signal characteristic listing may be astatic database 238 stored in the memory 230 of the spectrum managementdevice 202 which may correlate signal parameters and signalidentifications. In determination block 320 the processor 214 maydetermine whether the signal parameters of the identified signal orsignals match signal parameters in the characteristic listing 236. In anembodiment, a match may be determined based on the signal parametersbeing within a specified tolerance of one another. As an example, acenter frequency match may be determined when the center frequencies arewithin plus or minus 1 kHz of each other. In this manner, differencesbetween real world measured conditions of an identified signal and idealconditions listed in a characteristics listing may be accounted for inidentifying matches. If the signal parameters do not match (i.e.,determination block 320=“No”), in block 326 the processor 214 maydisplay an indication that the signal is unidentified on a display 242of the spectrum management device 202. In this manner, the user of thespectrum management device may be notified that a signal is detected,but has not been positively identified. If the signal parameters domatch (i.e., determination block 320=“Yes”), in block 324 the processor214 may display an indication of the signal identification on thedisplay 242. In an embodiment, the signal identification displayed maybe the signal identification correlated to the signal parameter in thesignal characteristic listing which matched the signal parameter for theidentified signal. Upon displaying the indications in blocks 324 or 326the processor 214 may return to block 302 and cyclically measure andidentify further signals of interest.

FIG. 4 illustrates an embodiment method 400 for measuring sample blocksof a radio frequency scan. In an embodiment the operations of method 400may be performed by the processor 214 of a spectrum management device202. As discussed above, in blocks 306 and 308 the processor 214 mayreceive RF energy measurements and convert the RF energy measurements tospectral representation data. In block 402 the processor 214 maydetermine a frequency range at which to sample the RF spectrum forsignals of interest. In an embodiment, a frequency range may be afrequency range of each sample block to be analyzed for potentialsignals. As an example, the frequency range may be 240 kHz. In anembodiment, the frequency range may be a default value. In anotherembodiment, the frequency range may be a user selectable value. In block404 the processor 214 may determine a number (N) of sample blocks tomeasure. In an embodiment, each sample block may be sized to thedetermined of default frequency range, and the number of sample blocksmay be determined by dividing the spectrum of the measured RF energy bythe frequency range. In block 406 the processor 214 may assign eachsample block a respective frequency range. As an example, if thedetermined frequency range is 240 kHz, the first sample block may beassigned a frequency range from 0 kHz to 240 kHz, the second sampleblock may be assigned a frequency range from 240 kHz to 480 kHz, etc. Inblock 408 the processor 214 may set the lowest frequency range sampleblock as the current sample block. In block 409 the processor 214 maymeasure the amplitude across the set frequency range for the currentsample block. As an example, at each frequency interval (such as 1 Hz)within the frequency range of the sample block the processor 214 maymeasure the received signal amplitude. In block 410 the processor 214may store the amplitude measurements and corresponding frequencies forthe current sample block. In determination block 414 the processor 214may determine if all sample blocks have been measured. If all sampleblocks have not been measured (i.e., determination block 414=“No”), inblock 416 the processor 214 may set the next highest frequency rangesample block as the current sample block. As discussed above, in blocks409, 410, and 414 the processor 214 may measure and store amplitudes anddetermine whether all blocks are sampled. If all blocks have beensampled (i.e., determination block 414=“Yes”), the processor 214 mayreturn to block 306 and cyclically measure further sample blocks.

FIGS. 5A, 5B, and 5C illustrate the process flow for an embodimentmethod 500 for determining signal parameters. In an embodiment, theoperations of method 500 may be performed by the processor 214 of aspectrum management device 202. Referring to FIG. 5A, in block 502 theprocessor 214 may receive a noise floor average setting. In anembodiment, the noise floor average setting may be an average noiselevel for the environment in which the spectrum management device 202 isoperating. In an embodiment, the noise floor average setting may be adefault setting and/or may be user selectable setting. In block 504 theprocessor 214 may receive the signal power threshold setting. In anembodiment, the signal power threshold setting may be an amplitudemeasure selected to distinguish RF energies associated with actualsignals from noise. In an embodiment, the signal power threshold may bea default value and/or may be a user selectable setting. In block 506the processor 214 may load the next available sample block. In anembodiment, the sample blocks may be assembled according to theoperations of method 400 described above with reference to FIG. 4. In anembodiment, the next available sample block may be an oldest in timesample block which has not been analyzed to determine whether signals ofinterest are present in the sample block. In block 508 the processor 214may average the amplitude measurements in the sample block. Indetermination block 510 the processor 214 may determine whether theaverage for the sample block is greater than or equal to the noise flooraverage set in block 502. In this manner, sample blocks includingpotential signals may be quickly distinguished from sample blocks whichmay not include potential signals reducing processing time by enablingsample blocks without potential signals to be identified and ignored. Ifthe average for the sample block is lower than the noise floor average(i.e., determination block 510=“No”), no signals of interest may bepresent in the current sample block. In determination block 514 theprocessor 214 may determine whether a cross block flag is set. If thecross block flag is not set (i.e., determination block 514=“No”), inblock 506 the processor 214 may load the next available sample block andin block 508 average the sample block 508.

If the average of the sample block is equal to or greater than the noisefloor average (i.e., determination block 510=“Yes”), the sample blockmay potentially include a signal of interest and in block 512 theprocessor 214 may reset a measurement counter (C) to 1. The measurementcounter value indicating which sample within a sample block is underanalysis. In determination block 516 the processor 214 may determinewhether the RF measurement of the next frequency sample (C) is greaterthan the signal power threshold. In this manner, the value of themeasurement counter (C) may be used to control which sample RFmeasurement in the sample block is compared to the signal powerthreshold. As an example, when the counter (C) equals 1, the first RFmeasurement may be checked against the signal power threshold and whenthe counter (C) equals 2 the second RF measurement in the sample blockmay be checked, etc. If the C RF measurement is less than or equal tothe signal power threshold (i.e., determination block 516=“No”), indetermination block 517 the processor 214 may determine whether thecross block flag is set. If the cross block flag is not set (i.e.,determination block 517=“No”), in determination block 522 the processor214 may determine whether the end of the sample block is reached. If theend of the sample block is reached (i.e., determination block522=“Yes”), in block 506 the processor 214 may load the next availablesample block and proceed in blocks 508, 510, 514, and 512 as discussedabove. If the end of the sample block is not reached (i.e.,determination block 522=“No”), in block 524 the processor 214 mayincrement the measurement counter (C) so that the next sample in thesample block is analyzed.

If the C RF measurement is greater than the signal power threshold(i.e., determination block 516=“Yes”), in block 518 the processor 214may check the status of the cross block flag to determine whether thecross block flag is set. If the cross block flag is not set (i.e.,determination block 518=“No”), in block 520 the processor 214 may set asample start. As an example, the processor 214 may set a sample start byindicating a potential signal of interest may be discovered in a memoryby assigning a memory location for RF measurements associated with thesample start. Referring to FIG. 5B, in block 526 the processor 214 maystore the C RF measurement in a memory location for the sample currentlyunder analysis. In block 528 the processor 214 may increment themeasurement counter (C) value.

In determination block 530 the processor 214 may determine whether the CRF measurement (e.g., the next RF measurement because the value of theRF measurement counter was incremented) is greater than the signal powerthreshold. If the C RF measurement is greater than the signal powerthreshold (i.e., determination block 530=“Yes”), in determination block532 the processor 214 may determine whether the end of the sample blockis reached. If the end of the sample block is not reached (i.e.,determination block 532=“No”), there may be further RF measurementsavailable in the sample block and in block 526 the processor 214 maystore the C RF measurement in the memory location for the sample. Inblock 528 the processor may increment the measurement counter (C) and indetermination block 530 determine whether the C RF measurement is abovethe signal power threshold and in block 532 determine whether the end ofthe sample block is reached. In this manner, successive sample RFmeasurements may be checked against the signal power threshold andstored until the end of the sample block is reached and/or until asample RF measurement falls below the signal power threshold. If the endof the sample block is reached (i.e., determination block 532=“Yes”), inblock 534 the processor 214 may set the cross block flag. In anembodiment, the cross block flag may be a flag in a memory available tothe processor 214 indicating the signal potential spans across two ormore sample blocks. In a further embodiment, prior to setting the crossblock flag in block 534, the slope of a line drawn between the last twoRF measurement samples may be used to determine whether the next sampleblock likely contains further potential signal samples. A negative slopemay indicate that the signal of interest is fading and may indicate thelast sample was the final sample of the signal of interest. In anotherembodiment, the slope may not be computed and the next sample block maybe analyzed regardless of the slope.

If the end of the sample block is reached (i.e., determination block532=“Yes”) and in block 534 the cross block flag is set, referring toFIG. 5A, in block 506 the processor 214 may load the next availablesample block, in block 508 may average the sample block, and in block510 determine whether the average of the sample block is greater than orequal to the noise floor average. If the average is equal to or greaterthan the noise floor average (i.e., determination block 510=“Yes”), inblock 512 the processor 214 may reset the measurement counter (C) to 1.In determination block 516 the processor 214 may determine whether the CRF measurement for the current sample block is greater than the signalpower threshold. If the C RF measurement is greater than the signalpower threshold (i.e., determination block 516=“Yes”), in determinationblock 518 the processor 214 may determine whether the cross block flagis set. If the cross block flag is set (i.e., determination block518=“Yes”), referring to FIG. 5B, in block 526 the processor 214 maystore the C RF measurement in the memory location for the sample and inblock 528 the processor may increment the measurement counter (C). Asdiscussed above, in blocks 530 and 532 the processor 214 may performoperations to determine whether the C RF measurement is greater than thesignal power threshold and whether the end of the sample block isreached until the C RF measurement is less than or equal to the signalpower threshold (i.e., determination block 530=“No”) or the end of thesample block is reached (i.e., determination block 532=“Yes”). If theend of the sample block is reached (i.e., determination block532=“Yes”), as discussed above in block 534 the cross block flag may beset (or verified and remain set if already set) and in block 535 the CRF measurement may be stored in the sample.

If the end of the sample block is reached (i.e., determination block532=“Yes”) and in block 534 the cross block flag is set, referring toFIG. 5A, the processor may perform operations of blocks 506, 508, 510,512, 516, and 518 as discussed above. If the average of the sample blockis less than the noise floor average (i.e., determination block510=“No”) and the cross block flag is set (i.e., determination block514=“Yes”), the C RF measurement is less than or equal to the signalpower threshold (i.e., determination block 516=“No”) and the cross blockflag is set (i.e., determination block 517=“Yes”), or the C RFmeasurement is less than or equal to the signal power threshold (i.e.,determination block 516=“No”), referring to FIG. 5B, in block 538 theprocessor 214 may set the sample stop. As an example, the processor 214may indicate that a sample end is reached in a memory and/or that asample is complete in a memory. In block 540 the processor 214 maycompute and store complex I and Q data for the stored measurements inthe sample. In block 542 the processor 214 may determine a mean of thecomplex I and Q data. Referring to FIG. 5C, in determination block 544the processor 214 may determine whether the mean of the complex I and Qdata is greater than a signal threshold. If the mean of the complex Iand Q data is less than or equal to the signal threshold (i.e.,determination block 544=“No”), in block 550 the processor 214 mayindicate the sample is noise and discard data associated with the samplefrom memory.

If the mean is greater than the signal threshold (i.e., determinationblock 544=“Yes”), in block 546 the processor 214 may identify the sampleas a signal of interest. In an embodiment, the processor 214 mayidentify the sample as a signal of interest by assigning a signalidentifier to the signal, such as a signal number or sample number. Inblock 548 the processor 214 may determine and store signal parametersfor the signal. As an example, the processor 214 may determine and storea frequency peak of the identified signal, a peak power of theidentified signal, an average power of the identified signal, a signalbandwidth of the identified signal, and/or a signal duration of theidentified signal. In block 552 the processor 214 may clear the crossblock flag (or verify that the cross block flag is unset). In block 556the processor 214 may determine whether the end of the sample block isreached. If the end of the sample block is not reached (i.e.,determination block 556=“No”) in block 558 the processor 214 mayincrement the measurement counter (C), and referring to FIG. 5A indetermination block 516 may determine whether the C RF measurement isgreater than the signal power threshold. Referring to FIG. 5C, if theend of the sample block is reached (i.e., determination block556=“Yes”), referring to FIG. 5A, in block 506 the processor 214 mayload the next available sample block.

FIG. 6 illustrates a process flow for an embodiment method 600 fordisplaying signal identifications. In an embodiment, the operations ofmethod 600 may be performed by a processor 214 of a spectrum managementdevice 202. In determination block 602 the processor 214 may determinewhether a signal is identified. If a signal is not identified (i.e.,determination block 602=“No”), in block 604 the processor 214 may waitfor the next scan. If a signal is identified (i.e., determination block602=“Yes”), in block 606 the processor 214 may compare the signalparameters of an identified signal to signal parameters in a historydatabase 232. In determination block 608 the processor 214 may determinewhether signal parameters of the identified signal match signalparameters in the history database 232. If there is no match (i.e.,determination block 608=“No”), in block 610 the processor 214 may storethe signal parameters as a new signal in the history database 232. Ifthere is a match (i.e., determination block 608=“Yes”), in block 612 theprocessor 214 may update the matching signal parameters as needed in thehistory database 232.

In block 614 the processor 214 may compare the signal parameters of theidentified signal to signal parameters in a signal characteristiclisting 236. In an embodiment, the characteristic listing 236 may be astatic database separate from the history database 232, and thecharacteristic listing 236 may correlate signal parameters with signalidentifications. In determination block 616 the processor 214 maydetermine whether the signal parameters of the identified signal matchany signal parameters in the signal characteristic listing 236. In anembodiment, the match in determination 616 may be a match based on atolerance between the signal parameters of the identified signal and theparameters in the characteristic listing 236. If there is a match (i.e.,determination block 616=“Yes”), in block 618 the processor 214 mayindicate a match in the history database 232 and in block 622 maydisplay an indication of the signal identification on a display 242. Asan example, the indication of the signal identification may be a displayof the radio call sign of an identified FM radio station signal. Ifthere is not a match (i.e., determination block 616=“No”), in block 620the processor 214 may display an indication that the signal is anunidentified signal. In this manner, the user may be notified a signalis present in the environment, but that the signal does not match to asignal in the characteristic listing.

FIG. 7 illustrates a process flow of an embodiment method 700 fordisplaying one or more open frequency. In an embodiment, the operationsof method 700 may be performed by the processor 214 of a spectrummanagement device 202. In block 702 the processor 214 may determine acurrent location of the spectrum management device 202. In anembodiment, the processor 214 may determine the current location of thespectrum management device 202 based on location inputs received from alocation receiver 212, such as GPS coordinates received from a GPSreceiver 212. In block 704 the processor 214 may compare the currentlocation to the stored location value in the historical database 232. Asdiscussed above, the historical or history database 232 may be adatabase storing information about signals previously actuallyidentified by the spectrum management device 202. In determination block706 the processor 214 may determine whether there are any matchesbetween the location information in the historical database 232 and thecurrent location. If there are no matches (i.e., determination block706=“No”), in block 710 the processor 214 may indicate incomplete datais available. In other words the spectrum data for the current locationhas not previously been recorded.

If there are matches (i.e., determination block 706=“Yes”), in optionalblock 708 the processor 214 may display a plot of one or more of thesignals matching the current location. As an example, the processor 214may compute the average frequency over frequency intervals across agiven spectrum and may display a plot of the average frequency over eachinterval. In block 712 the processor 214 may determine one or more openfrequencies at the current location. As an example, the processor 214may determine one or more open frequencies by determining frequencyranges in which no signals fall or at which the average is below athreshold. In block 714 the processor 214 may display an indication ofone or more open frequency on a display 242 of the spectrum managementdevice 202.

FIG. 8A is a block diagram of a spectrum management device 802 accordingto an embodiment. Spectrum management device 802 is similar to spectrummanagement device 202 described above with reference to FIG. 2A, exceptthat spectrum management device 802 may include symbol module 816 andprotocol module 806 enabling the spectrum management device 802 toidentify the protocol and symbol information associated with anidentified signal as well as protocol match module 814 to match protocolinformation. Additionally, the characteristic listing 236 of spectrummanagement device 802 may include protocol data 804, hardware data 808,environment data 810, and noise data 812 and an optimization module 818may enable the signal processor 214 to provide signal optimizationparameters.

The protocol module 806 may identify the communication protocol (e.g.,LTE, CDMA, etc.) associated with a signal of interest. In an embodiment,the protocol module 806 may use data retrieved from the characteristiclisting, such as protocol data 804 to help identify the communicationprotocol. The symbol detector module 816 may determine symbol timinginformation, such as a symbol rate for a signal of interest. Theprotocol module 806 and/or symbol module 816 may provide data to thecomparison module 222. The comparison module 222 may include a protocolmatch module 814 which may attempt to match protocol information for asignal of interest to protocol data 804 in the characteristic listing toidentify a signal of interest. Additionally, the protocol module 806and/or symbol module 816 may store data in the memory module 226 and/orhistory database 232. In an embodiment, the protocol module 806 and/orsymbol module 816 may use protocol data 804 and/or other data from thecharacteristic listing 236 to help identify protocols and/or symbolinformation in signals of interest.

The optimization module 818 may gather information from thecharacteristic listing, such as noise figure parameters, antennahardware parameters, and environmental parameters correlated with anidentified signal of interest to calculate a degradation value for theidentified signal of interest. The optimization module 818 may furthercontrol the display 242 to output degradation data enabling a user ofthe spectrum management device 802 to optimize a signal of interest.

FIG. 8B is a schematic logic flow block diagram illustrating logicaloperations which may be performed by a spectrum management deviceaccording to an embodiment. Only those logical operations illustrated inFIG. 8B different from those described above with reference to FIG. 2Bwill be discussed. As illustrated in FIG. 8B, as received time tracking850 may be applied to the I and Q data from the receiver 210. Anadditional buffer 851 may further store the I and Q data received and asymbol detector 852 may identify the symbols of a signal of interest anddetermine the symbol rate. A multiple access scheme identifier module854 may identify whether the signal is part of a multiple access scheme(e.g., CDMA), and a protocol identifier module 856 may attempt toidentify the protocol the signal of interest is associated with. Themultiple access scheme identifier module 854 and protocol identifiermodule 856 may retrieve data from the static database 238 to aid in theidentification of the access scheme and/or protocol. The symbol detectormodule 852 may pass data to the signal parameters and protocols module858 which may store protocol and symbol information in addition tosignal parameter information for signals of interest.

FIG. 9 illustrates a process flow of an embodiment method 900 fordetermining protocol data and symbol timing data. In an embodiment, theoperations of method 900 may be performed by the processor 214 of aspectrum management device 802. In determination block 902 the processor214 may determine whether two or more signals are detected. If two ormore signals are not detected (i.e., determination block 902=“No”), indetermination block 902 the processor 214 may continue to determinewhether two or more signals are detected. If two or more signals aredetected (i.e., determination block 902=“Yes”), in determination block904 the processor 214 may determine whether the two or more signals areinterrelated. In an embodiment, a mean correlation value of the spectraldecomposition of each signal may indicate the two or more signals areinterrelated. As an example, a mean correlation of each signal maygenerate a value between 0.0 and 1, and the processor 214 may comparethe mean correlation value to a threshold, such as a threshold of 0.75.In such an example, a mean correlation value at or above the thresholdmay indicate the signals are interrelated while a mean correlation valuebelow the threshold may indicate the signals are not interrelated andmay be different signals. In an embodiment, the mean correlation valuemay be generated by running a full energy bandwidth correlation of eachsignal, measuring the values of signal transition for each signal, andfor each signal transition running a spectral correlation betweensignals to generate the mean correlation value. If the signals are notinterrelated (i.e., determination block 904=“No”), the signals may betwo or more different signals, and in block 907 processor 214 maymeasure the interference between the two or more signals. In an optionalembodiment, in optional block 909 the processor 214 may generate aconflict alarm indicating the two or more different signals interfere.In an embodiment, the conflict alarm may be sent to the history databaseand/or a display. In determination block 902 the processor 214 maycontinue to determine whether two or more signals are detected. If thetwo signal are interrelated (i.e., determination block 904=“Yes”), inblock 905 the processor 214 may identify the two or more signals as asingle signal. In block 906 the processor 214 may combine signal datafor the two or more signals into a signal single entry in the historydatabase. In determination block 908 the processor 214 may determinewhether the signals mean averages. If the mean averages (i.e.,determination block 908=“Yes”), the processor 214 may identify thesignal as having multiple channels in block 910. If the mean does notaverage (i.e., determination block 908=“No”) or after identifying thesignal as having multiple channels, in block 914 the processor 214 maydetermine and store protocol data for the signal. In block 916 theprocessor 214 may determine and store symbol timing data for the signal,and the method 900 may return to block 902.

FIG. 10 illustrates a process flow of an embodiment method 1000 forcalculating signal degradation data. In an embodiment, the operations ofmethod 1000 may be performed by the processor 214 of a spectrummanagement device 202. In block 1002 the processor may detect a signal.In block 1004 the processor 214 may match the signal to a signal in astatic database. In block 1006 the processor 214 may determine noisefigure parameters based on data in the static database 236 associatedwith the signal. As an example, the processor 214 may determine thenoise figure of the signal based on parameters of a transmitteroutputting the signal according to the static database 236. In block1008 the processor 214 may determine hardware parameters associated withthe signal in the static database 236. As an example, the processor 214may determine hardware parameters such as antenna position, powersettings, antenna type, orientation, azimuth, location, gain, andequivalent isotropically radiated power (EIRP) for the transmitterassociated with the signal from the static database 236. In block 1010processor 214 may determine environment parameters associated with thesignal in the static database 236. As an example, the processor 214 maydetermine environment parameters such as rain, fog, and/or haze based ona delta correction factor table stored in the static database and aprovided precipitation rate (e.g., mm/hr). In block 1012 the processor214 may calculate and store signal degradation data for the detectedsignal based at least in part on the noise figure parameters, hardwareparameters, and environmental parameters. As an example, based on thenoise figure parameters, hardware parameters, and environmentalparameters free space losses of the signal may be determined. In block1014 the processor 214 may display the degradation data on a display 242of the spectrum management device 202. In a further embodiment, thedegradation data may be used with measured terrain data of geographiclocations stored in the static database to perform pattern distortion,generate propagation and/or next neighbor interference models, determineinterference variables, and perform best fit modeling to aide in signaland/or system optimization.

FIG. 11 illustrates a process flow of an embodiment method 1100 fordisplaying signal and protocol identification information. In anembodiment, the operations of method 1100 may be performed by aprocessor 214 of a spectrum management device 202. In block 1102 theprocessor 214 may compare the signal parameters and protocol data of anidentified signal to signal parameters and protocol data in a historydatabase 232. In an embodiment, a history database 232 may be a databasestoring signal parameters and protocol data for previously identifiedsignals. In block 1104 the processor 214 may determine whether there isa match between the signal parameters and protocol data of theidentified signal and the signal parameters and protocol data in thehistory database 232. If there is not a match (i.e., determination block1104=“No”), in block 1106 the processor 214 may store the signalparameters and protocol data as a new signal in the history database232. If there is a match (i.e., determination block 1104=“Yes”), inblock 1108 the processor 214 may update the matching signal parametersand protocol data as needed in the history database 232.

In block 1110 the processor 214 may compare the signal parameters andprotocol data of the identified signal to signal parameters and protocoldata in the signal characteristic listing 236. In determination block1112 the processor 214 may determine whether the signal parameters andprotocol data of the identified signal match any signal parameters andprotocol data in the signal characteristic listing 236. If there is amatch (i.e., determination block 1112=“Yes”), in block 1114 theprocessor 214 may indicate a match in the history database and in block1118 may display an indication of the signal identification and protocolon a display. If there is not a match (i.e., determination block1112=“No”), in block 1116 the processor 214 may display an indicationthat the signal is an unidentified signal. In this manner, the user maybe notified a signal is present in the environment, but that the signaldoes not match to a signal in the characteristic listing.

FIG. 12A is a block diagram of a spectrum management device 1202according to an embodiment. Spectrum management device 1202 is similarto spectrum management device 802 described above with reference to FIG.8A, except that spectrum management device 1202 may include TDOA/FDOAmodule 1204 and modulation module 1206 enabling the spectrum managementdevice 1202 to identify the modulation type employed by a signal ofinterest and calculate signal origins. The modulation module 1206 mayenable the signal processor to determine the modulation applied tosignal, such as frequency modulation (e.g., FSK, MSK, etc.) or phasemodulation (e.g., BPSK, QPSK, QAM, etc.) as well as to demodulate thesignal to identify payload data carried in the signal. The modulationmodule 1206 may use payload data 1221 from the characteristic listing toidentify the data types carried in a signal. As examples, upondemodulating a portion of the signal the payload data may enable theprocessor 214 to determine whether voice data, video data, and/or textbased data is present in the signal. The TDOA/FDOA module 1204 mayenable the signal processor 214 to determine time difference of arrivalfor signals or interest and/or frequency difference of arrival forsignals of interest. Using the TDOA/FDOA information estimates of theorigin of a signal may be made and passed to a mapping module 1225 whichmay control the display 242 to output estimates of a position and/ordirection of movement of a signal.

FIG. 12B is a schematic logic flow block diagram illustrating logicaloperations which may be performed by a spectrum management deviceaccording to an embodiment. Only those logical operations illustrated inFIG. 12B different from those described above with reference to FIG. 8Bwill be discussed. A time tracking operation 1250 may be applied to theI and Q data from the receiver 210, by a time tracking module, such as aTDOA/FDOA module. A magnitude squared 1252 operation may be performed ondata from the symbol detector 852 to identify whether frequency or phasemodulation is present in the signal. Phase modulated signals may beidentified by the phase modulation 1254 processes and frequencymodulated signals may be identified by the frequency modulation 1256processes. The modulation information may be passed to a signalparameters, protocols, and modulation module 1258.

FIG. 13 illustrates a process flow of an embodiment method 1300 forestimating a signal origin based on a frequency difference of arrival.In an embodiment, the operations of method 1300 may be performed by aprocessor 214 of a spectrum management device 1202. In block 1302 theprocessor 214 may compute frequency arrivals and phase arrivals formultiple instances of an identified signal. In block 1304 the processor214 may determine frequency difference of arrival for the identifiedsignal based on the computed frequency difference and phase difference.In block 1306 the processor may compare the determined frequencydifference of arrival for the identified signal to data associated withknown emitters in the characteristic listing to estimate an identifiedsignal origin. In block 1308 the processor 214 may indicate theestimated identified signal origin on a display of the spectrummanagement device. As an example, the processor 214 may overlay theestimated origin on a map displayed by the spectrum management device.

FIG. 14 illustrates a process flow of an embodiment method fordisplaying an indication of an identified data type within a signal. Inan embodiment, the operations of method 1400 may be performed by aprocessor 214 of a spectrum management device 1202. In block 1402 theprocessor 214 may determine the signal parameters for an identifiedsignal of interest. In block 1404 the processor 214 may determine themodulation type for the signal of interest. In block 1406 the processor214 may determine the protocol data for the signal of interest. In block1408 the processor 214 may determine the symbol timing for the signal ofinterest. In block 1410 the processor 214 may select a payload schemebased on the determined signal parameters, modulation type, protocoldata, and symbol timing. As an example, the payload scheme may indicatehow data is transported in a signal. For example, data in over the airtelevision broadcasts may be transported differently than data incellular communications and the signal parameters, modulation type,protocol data, and symbol timing may identify the applicable payloadscheme to apply to the signal. In block 1412 the processor 214 may applythe selected payload scheme to identify the data type or types withinthe signal of interest. In this manner, the processor 214 may determinewhat type of data is being transported in the signal, such as voicedata, video data, and/or text based data. In block 1414 the processormay store the data type or types. In block 1416 the processor 214 maydisplay an indication of the identified data types.

FIG. 15 illustrates a process flow of an embodiment method 1500 fordetermining modulation type, protocol data, and symbol timing data.Method 1500 is similar to method 900 described above with reference toFIG. 9, except that modulation type may also be determined. In anembodiment, the operations of method 1500 may be performed by aprocessor 214 of a spectrum management device 1202. In blocks 902, 904,905, 906, 908, and 910 the processor 214 may perform operations of likenumbered blocks of method 900 described above with reference to FIG. 9.In block 1502 the processor may determine and store a modulation type.As an example, a modulation type may be an indication that the signal isfrequency modulated (e.g., FSK, MSK, etc.) or phase modulated (e.g.,BPSK, QPSK, QAM, etc.). As discussed above, in block 914 the processormay determine and store protocol data and in block 916 the processor maydetermine and store timing data.

In an embodiment, based on signal detection, a time tracking module,such as a TDOA/FDOA module 1204, may track the frequency repetitioninterval at which the signal is changing. The frequency repetitioninterval may also be tracked for a burst signal. In an embodiment, thespectrum management device may measure the signal environment and setanchors based on information stored in the historic or static databaseabout known transmitter sources and locations. In an embodiment, thephase information about a signal be extracted using a spectraldecomposition correlation equation to measure the angle of arrival(“AOA”) of the signal. In an embodiment, the processor of the spectrummanagement device may determine the received power as the ReceivedSignal Strength (“RSS”) and based on the AOA and RSS may measure thefrequency difference of arrival. In an embodiment, the frequency shiftof the received signal may be measured and aggregated over time. In anembodiment, after an initial sample of a signal, known transmittedsignals may be measured and compared to the RSS to determine frequencyshift error. In an embodiment, the processor of the spectrum managementdevice may compute a cross ambiguity function of aggregated changes inarrival time and frequency of arrival. In an additional embodiment, theprocessor of the spectrum management device may retrieve FFT data for ameasured signal and aggregate the data to determine changes in time ofarrival and frequency of arrival. In an embodiment, the signalcomponents of change in frequency of arrival may be averaged through aKalman filter with a weighted tap filter from 2 to 256 weights to removemeasurement error such as noise, multipath interference, etc. In anembodiment, frequency difference of arrival techniques may be appliedwhen either the emitter of the signal or the spectrum management deviceare moving or when then emitter of the signal and the spectrummanagement device are both stationary. When the emitter of the signaland the spectrum management device are both stationary the determinationof the position of the emitter may be made when at least four knownother known signal emitters positions are known and signalcharacteristics may be available. In an embodiment, a user may providethe four other known emitters and/or may use already in place knownemitters, and may use the frequency, bandwidth, power, and distancevalues of the known emitters and their respective signals. In anembodiment, where the emitter of the signal or spectrum managementdevice may be moving, frequency deference of arrival techniques may beperformed using two known emitters.

FIG. 16 illustrates an embodiment method for tracking a signal origin.In an embodiment, the operations of method 1600 may be performed by aprocessor 214 of a spectrum management device 1202. In block 1602 theprocessor 214 may determine a time difference of arrival for a signal ofinterest. In block 1604 the processor 214 may determine a frequencydifference of arrival for the signal interest. As an example, theprocessor 214 may take the inverse of the time difference of arrival todetermine the frequency difference of arrival of the signal of interest.In block 1606 the processor 214 may identify the location. As anexample, the processor 214 may determine the location based oncoordinates provided from a GPS receiver. In determination block 1608the processor 214 may determine whether there are at least four knownemitters present in the identified location. As an example, theprocessor 214 may compare the geographic coordinates for the identifiedlocation to a static database and/or historical database to determinewhether at least four known signals are within an area associated withthe geographic coordinates. If at least four known emitters are present(i.e., determination block 1608=“Yes”), in block 1612 the processor 214may collect and measure the RSS of the known emitters and the signal ofinterest. As an example, the processor 214 may use the frequency,bandwidth, power, and distance values of the known emitters and theirrespective signals and the signal of interest. If less than four knownemitters are present (i.e., determination block 1608=“No”), in block1610 the processor 214 may measure the angle of arrival for the signalof interest and the known emitter. Using the RSS or angle or arrival, inblock 1614 the processor 214 may measure the frequency shift and inblock 1616 the processor 214 may obtain the cross ambiguity function. Indetermination block 1618 the processor 214 may determine whether thecross ambiguity function converges to a solution. If the cross ambiguityfunction does converge to a solution (i.e., determination block1618=“Yes”), in block 1620 the processor 214 may aggregate the frequencyshift data. In block 1622 the processor 214 may apply one or more filterto the aggregated data, such as a Kalman filter. Additionally, theprocessor 214 may apply equations, such as weighted least squaresequations and maximum likelihood equations, and additional filters, suchas a non-line-of-sight (“NLOS”) filters to the aggregated data. In anembodiment, the cross ambiguity function may resolve the position of theemitter of the signal of interest to within 3 meters. If the crossambiguity function does not converge to a solution (i.e., determinationblock 1618=“No”), in block 1624 the processor 214 may determine the timedifference of arrival for the signal and in block 1626 the processor 214may aggregate the time shift data. Additionally, the processor mayfilter the data to reduce interference. Whether based on frequencydifference of arrival or time difference of arrival, the aggregated andfiltered data may indicate a position of the emitter of the signal ofinterest, and in block 1628 the processor 214 may output the trackinginformation for the position of the emitter of the signal of interest toa display of the spectrum management device and/or the historicaldatabase. In an additional embodiment, location of emitters, time andduration of transmission at a location may be stored in the historydatabase such that historical information may be used to perform andpredict movement of signal transmission. In a further embodiment, theenvironmental factors may be considered to further reduce the measurederror and generate a more accurate measurement of the location of theemitter of the signal of interest.

The processor 214 of spectrum management devices 202, 802 and 1202 maybe any programmable microprocessor, microcomputer or multiple processorchip or chips that can be configured by software instructions(applications) to perform a variety of functions, including thefunctions of the various embodiments described above. In some devices,multiple processors may be provided, such as one processor dedicated towireless communication functions and one processor dedicated to runningother applications. Typically, software applications may be stored inthe internal memory 226 or 230 before they are accessed and loaded intothe processor 214. The processor 214 may include internal memorysufficient to store the application software instructions. In manydevices the internal memory may be a volatile or nonvolatile memory,such as flash memory, or a mixture of both. For the purposes of thisdescription, a general reference to memory refers to memory accessibleby the processor 214 including internal memory or removable memoryplugged into the device and memory within the processor 214 itself.

Identifying Devices in White Space.

The present invention provides for systems, methods, and apparatussolutions for device sensing in white space, which improves upon theprior art by identifying sources of signal emission by automaticallydetecting signals and creating unique signal profiles. Device sensinghas an important function and applications in military and otherintelligence sectors, where identifying the emitter device is crucialfor monitoring and surveillance, including specific emitteridentification (SEI).

At least two key functions are provided by the present invention: signalisolation and device sensing. Signal Isolation according to the presentinvention is a process whereby a signal is detected, isolated throughfiltering and amplification, amongst other methods, and keycharacteristics extracted. Device Sensing according to the presentinvention is a process whereby the detected signals are matched to adevice through comparison to device signal profiles and may includeapplying a confidence level and/or rating to the signal-profilematching. Further, device sensing covers technologies that permitstorage of profile comparisons such that future matching can be donewith increased efficiency and/or accuracy. The present inventionsystems, methods, and apparatus are constructed and configuredfunctionally to identify any signal emitting device, including by way ofexample and not limitation, a radio, a cell phone, etc.

Regarding signal isolation, the following functions are included in thepresent invention: amplifying, filtering, detecting signals throughenergy detection, waveform-based, spectral correlation-based, radioidentification-based, or matched filter method, identifyinginterference, identifying environmental baseline(s), and/or identifysignal characteristics.

Regarding device sensing, the following functions are included in thepresent invention: using signal profiling and/or comparison with knowndatabase(s) and previously recorded profile(s), identifying the expecteddevice or emitter, stating the level of confidence for theidentification, and/or storing profiling and sensing information forimproved algorithms and matching. In one embodiment, the expected deviceis a signal emitting device. In another embodiment, the expected deviceis a signal reflecting device. Visible light and infrared are both partof the electromagnetic radiation as the RF spectrum is. In preferredembodiments of the present invention, the identification of the at leastone signal emitting device and/or the at least one signal reflectingdevice is accurate to a predetermined degree of confidence between about80 and about 95 percent, and more preferably between about 80 and about100 percent. The confidence level or degree of confidence is based uponthe amount of matching measured data compared with historical dataand/or reference data for predetermined frequency and othercharacteristics. Additionally or alternatively, the confidence level ordegree of confidence is based upon the comparison of measured spectrumdata and video data or reflecting data.

The present invention provides for wireless signal-emitting devicesensing in the white space based upon a measured signal, and considersthe basis of license(s) provided in at least one reference database,preferably the federal communication commission (FCC) and/or otherdefined database including license listings. The methods include thesteps of providing a device for measuring characteristics of signalsfrom signal emitting devices in a spectrum associated with wirelesscommunications, the characteristics of the measured data from the signalemitting devices including frequency, power, bandwidth, duration,modulation, and combinations thereof; making an assessment orcategorization on analog and/or digital signal(s); determining the bestfit based on frequency if the measured power spectrum is designated inhistorical and/or reference data, including but not limited to the FCCor other database(s) for select frequency ranges; determining analog ordigital, based on power and sideband combined with frequency allocation;determining a TDM/FDM/CDM signal, based on duration and bandwidth;determining best modulation fit for the desired signal, if the bandwidthand duration match the signal database(s); adding modulationidentification to the database; listing possible modulations with bestpercentage fit, based on the power, bandwidth, frequency, duration,database allocation, and combinations thereof; and identifying at leastone signal emitting device from the composite results of the foregoingsteps. Additionally, the present invention provides that the phasemeasurement of the signal is calculated between the difference of theend frequency of the bandwidth and the peak center frequency and thestart frequency of the bandwidth and the peak center frequency to get abetter measurement of the sideband drop off rate of the signal to helpdetermine the modulation of the signal.

In embodiments of the present invention, an apparatus is provided forautomatically identifying devices in a spectrum, the apparatus includinga housing, at least one processor and memory, and sensors constructedand configured for sensing and measuring wireless communications signalsfrom signal emitting devices in a spectrum associated with wirelesscommunications; and wherein the apparatus is operable to automaticallyanalyze the measured data to identify at least one signal emittingdevice in near real time from attempted detection and identification ofthe at least one signal emitting device. The characteristics of signalsand measured data from the signal emitting devices include frequency,power, bandwidth, duration, modulation, and combinations thereof.

The present invention systems including at least one apparatus, whereinthe at least one apparatus is operable for network-based communicationwith at least one server computer including a database, and/or with atleast one other apparatus, but does not require a connection to the atleast one server computer to be operable for identifying signal emittingdevices; wherein each of the apparatus is operable for identifyingsignal emitting devices including: a housing, at least one processor andmemory, and sensors constructed and configured for sensing and measuringwireless communications signals from signal emitting devices in aspectrum associated with wireless communications; and wherein theapparatus is operable to automatically analyze the measured data toidentify at least one signal emitting device in near real time fromattempted detection and identification of the at least one signalemitting device.

Identifying Open Space in a Wireless Communication Spectrum.

The present invention provides for systems, methods, and apparatussolutions for automatically identifying open space, including open spacein the white space of a wireless communication spectrum. Importantly,the present invention identifies the open space as the space that isunused and/or seldomly used (and identifies the owner of the licensesfor the seldomly used space, if applicable), including unlicensedspectrum, white space, guard bands, and combinations thereof. Methodsteps of the present invention include: automatically obtaining alisting or report of all frequencies in the frequency range; plotting aline and/or graph chart showing power and bandwidth activity; settingfrequencies based on a frequency step and/or resolution so that onlyuser-defined frequencies are plotted; generating files, such as by wayof example and not limitation, .csv or .pdf files, showing averageand/or aggregated values of power, bandwidth and frequency for eachderived frequency step; and showing an activity report over time, overday vs. night, over frequency bands if more than one, in white space ifrequested, in Industrial, Scientific, and Medical (ISM) band or space ifrequested; and if frequency space is seldomly in that area, thenidentify and list frequencies and license holders.

Additional steps include: automatically scanning the frequency span,wherein a default scan includes a frequency span between about 54 MHzand about 804 MHz; an ISM scan between about 900 MHz and about 2.5 GHz;an ISM scan between about 5 GHz and about 5.8 GHz; and/or a frequencyrange based upon inputs provided by a user. Also, method steps includescanning for an allotted amount of time between a minimum of about 15minutes up to about 30 days; preferably scanning for allotted timesselected from the following: a minimum of about 15 minutes; about 30minutes; about 1 hour increments; about 5 hour increments; about 10 hourincrements; about 24 hours; about 1 day; and about up to 30 days; andcombinations thereof. In preferred embodiments, if the apparatus isconfigured for automatically scanning for more than about 15 minutes,then the apparatus is preferably set for updating results, includingupdating graphs and/or reports for an approximately equal amount of time(e.g., every 15 minutes).

The systems, methods, and apparatus also provide for automaticallycalculating a percent activity associated with the identified open spaceon predetermined frequencies and/or ISM bands.

Signal Database.

Preferred embodiments of the present invention provide for sensed and/ormeasured data received by the at least one apparatus of the presentinvention, analyzed data, historical data, and/or reference data,change-in-state data, and any updates thereto, are storable on each ofthe at least one apparatus. In systems of the present invention, eachapparatus further includes transmitters for sending the sensed and/ormeasured data received by the at least one apparatus of the presentinvention, analyzed data, historical data, and/or reference data,change-in-state data, and any updates thereto, are communicated via thenetwork to the at least one remote server computer and its correspondingdatabase(s). Preferably, the server(s) aggregate the data received fromthe multiplicity of apparatus or devices to produce a composite databasefor each of the types of data indicated. Thus, while each of theapparatus or devices is fully functional and self-contained within thehousing for performing all method steps and operations withoutnetwork-based communication connectivity with the remote server(s), whenconnected, as illustrated in FIG. 29, the distributed devices providethe composite database, which allows for additional analytics notpossible for individual, isolated apparatus or device units (when notconnected in network-based communication), which solves a longstanding,unmet need.

In particular, the aggregation of data from distributed, differentapparatus or device units allow for comparison of sample sets of data tocompare signal data or information for similar factors, includingtime(s), day(s), venues, geographic locations or regions, situations,activities, etc., as well as for comparing various signalcharacteristics with the factors, wherein the signal characteristics andtheir corresponding sensed and/or measured data, including raw data andchange-in-state data, and/or analyzed data from the signal emittingdevices include frequency, power, bandwidth, duration, modulation, andcombinations thereof. Preferably, the comparisons are conducted in nearreal time. The aggregation of data may provide for information about thesame or similar mode from apparatus to apparatus, scanning the same ordifferent frequency ranges, with different factors and/or signalcharacteristics received and stored in the database(s), both on eachapparatus or device unit, and when they are connected in network-basedcommunication for transmission of the data to the at least one remoteserver.

The aggregation of data from a multiplicity of units also advantageouslyprovides for continuous monitoring, e.g., for 24 hours continuously 7days per week scanning, measuring and/or monitoring of the RF spectrumfor wireless communications, and allows the system to identify sectionsthat exist as well as possibly omitted information or lost data, whichmay still be considered for comparisons, even if it is incomplete. Froma time standpoint, there may not be a linearity with respect to whendata is collected or received by the units; rather, the systems andmethods of the present invention provide for automated matching of time,i.e., matching time frames and relative times, even where theenvironment, activities, and/or context may be different for differentunits. By way of example and not limitation, different units may senseand/or measure the same signal from the same signal emitting device inthe spectrum, but interference, power, environmental factors, and otherfactors may present identification issues that preclude one of the atlast one apparatus or device units from determining the identity of thesignal emitting device with the same degree of certainty or confidence.The variation in this data from a multiplicity of units measuring thesame signals provides for aggregation and comparison at the remoteserver using the distributed databases from each unit to generate avariance report in near real time. Thus, the database(s) providerepository database in memory on the apparatus or device units, and/ordata from a multiplicity of units are aggregated on at least one remoteserver to provide an active network with distributed nodes over a regionthat produce an active or dynamic database of signals, identifieddevices, identified open space, and combinations thereof, and the nodesmay report to or transmit data via network-based communication to acentral hub or server. This provides for automatically comparing signalemitting devices or their profiles and corresponding sensed or measureddata, situations, activities, geographies, times, days, and/orenvironments, which provides unique composite and comparison data thatmay be continuously updated.

FIG. 29 shows a schematic diagram illustrating aspects of the systems,methods and apparatus according to the present invention. Each nodeincludes an apparatus or device unit, referenced in the FIG. 29 as“SigSet Device A”, “SigSet Device B”, “SigSet Device C”, and through“SigSet Device N” that are constructed and configured for selectiveexchange, both transmitting and receiving information over a networkconnection, either wired or wireless communications, with the masterSigDB or database at a remote server location from the units.

Furthermore, the database aggregating nodes of the apparatus or deviceunits provide a baseline compared with new data, which provide for nearreal time analysis and results within each of the at least one apparatusor device unit, which calculates and generates results such as signalemitting device identification, identification of open space, signaloptimization, and combinations thereof, based upon the particularsettings of each of the at least one apparatus or device unit. Thesettings include frequency ranges, location and distance from otherunits, difference in propagation from one unit to another unit, andcombinations thereof, which factor into the final results.

The present invention systems, methods, and apparatus embodimentsprovide for leveraging the use of deltas or differentials from thebaseline, as well as actual data, to provide onsite sensing,measurement, and analysis for a given environment and spectrum, for eachof the at least one apparatus or device unit. Because the presentinvention provides the at least one processor on each unit to comparesignals and signal characteristic differences using compressed data fordeltas to provide near real time results, the database storage mayfurther be optimized by storing compressed data and/or deltas, and thendecompressing and/or reconstructing the actual signals using the deltasand the baseline. Analytics are also provided using this approach. Sothen the signals database(s) provide for reduced data storage to thesmallest sample set that still provides at least the baseline and thedeltas to enable signal reconstruction and analysis to produce theresults described according to the present invention.

Preferably, the modeling and virtualization analytics enabled by thedatabases on each of the at least one apparatus or device unitsindependently of the remote server computer, and also provided on theremote server computer from aggregated data, provide for “gap filling”for omitted or absent data, and or for reconstruction from deltas. Amultiplicity of deltas may provide for signal identification,interference identification, neighboring band identification, deviceidentification, signal optimization, and combinations, all in near realtime. Significantly, the deltas approach of the present invention whichprovide for minimization of data sets or sample data sets required forcomparisons and/or analytics, i.e., the smallest range of time,frequency, etc. that captures all representative signals and/or deltasassociated with the signals, environment conditions, noise, etc.

The signal database(s) may be represented with visual indicationsincluding diagrams, graphs, plots, tables, and combinations thereof,which may be presented directly by the apparatus or device unit to itscorresponding display contained within the housing. Also, the signalsdatabase(s) provide each apparatus or device unit to receive a firstsample data set in a first time period, and receive a second sample dataset in a second time period, and receive a N sample data set in acorresponding N time period; to save or store each of the at least twodistinct sample data sets; to automatically compare the at least twosample data sets to determine a change-in-state or “delta”. Preferably,the database receives and stores at least the first of the at least twodata sets and also stores the delta. The stored delta values provide forquick analytics and regeneration of the actual values of the sample setsfrom the delta values, which advantageously contributes to the near realtime results of the present invention.

In preferred embodiments of the present invention, the at least oneapparatus is continuously scanning the environment for signals, deltasfrom prior at least one sample data set, and combinations, which arecategorized, classified, and stored in memory.

The systems, methods and apparatus embodiments of the present inventioninclude hardware and software components that are constructed andconfigured within the apparatus units or devices to operate to senseand/or measure signal data within the RF spectrum for wirelesscommunication, wherein the units or devices are further operable tocommunicate with each other and/or at least one remote server for dataprocessing, analysis, and/or storage. Thus, the apparatus units ordevices are configured for wireless cross-communication with at leastone other device to connect and communicate the data they sense,measure, analyze, and/or store on local database(s) in memory on theunits or devices, and to connect and communicate or transmit data forprocessing and/or storage with the remote server computer and/ordatabase. Thus the master database or “SigDB” is operable to be appliedand connect to the units, and may include hardware and softwarecommercially available, for example SQL Server 2012, and to be appliedto provide a user the criteria to upgrade/update their current severnetwork to the correct configuration that is required to operate andaccess the SigDB. Also, the SigDB is preferably designed, constructedand as a full hardware and software system configuration for the user,including load testing and network security and configuration. Otherexemplary requirements include that the SigDB will include a databasestructure that can sustain a multiplicity of apparatus units'information; provide a method to update the FCC database and/orhistorical database according a set time (every month/quarter/week,etc.), and in accordance with changes to the FCC.gov databases that areintegrated into the database; operable to receive and to download unitdata from a remote location through a network connection; be operable toquery apparatus unit data stored within the SigDB database server and toquery apparatus unit data in ‘present’ time to a particular apparatusunit device for a given ‘present’ time not available in the currentSigDB server database; update this information into its own databasestructure; to keep track of Device Identifications and the informationeach apparatus unit is collecting including its location; to query theapparatus units based on Device ID or location of device or apparatusunit; to connect to several devices and/or apparatus units on adistributed communications network; to partition data from eachapparatus unit or device and differentiate the data from each based onits location and Device ID; to join queries from several devices if auser wants to know information acquired from several remote apparatusunits at a given time; to provide ability for several users (currentlyup to 5 per apparatus unit or device) to query information from theSigDB database or apparatus unit or device; to grant access permissionsto records for each user based on device ID, pertinent information ortables/location; to connect to a user GUI from a remote device such as aworkstation or tablet PC from a Web App application; to retrieve dataqueries based on user information and/or jobs; to integrate databaseexternal database information from the apparatus units; and combinationsthereof.

Also, in preferred embodiments, a GUI interface based on a WebApplication software is provided; in one embodiment, the SigDB GUI isprovided in any appropriate software, such as by way of example, inVisual Studio using .Net/Asp.Net technology or JavaScript. In any case,the SigDB GUI preferably operates across cross platform systems withcorrect browser and operating system (OS) configuration; provides theinitial requirements of a History screen in each apparatus unit toaccess sever information or query a remote apparatus unit containing thedesired user information; and, generates .csv and .pdf reports that areuseful to the user.

Automated Reports and Visualization of Analytics.

Various reports for describing and illustrating with visualization thedata and analysis of the device, system and method results from spectrummanagement activities include at least reports on power usage, RFsurvey, and/or variance, as well as interference detection,intermodulation detection, uncorrelated licenses, and/or open spaceidentification.

The systems, methods, and devices of the various embodiments enablespectrum management by identifying, classifying, and cataloging signalsof interest based on radio frequency measurements. In an embodiment,signals and the parameters of the signals may be identified andindications of available frequencies may be presented to a user. Inanother embodiment, the protocols of signals may also be identified. Ina further embodiment, the modulation of signals, devices or device typesemitting signals, data types carried by the signals, and estimatedsignal origins may be identified.

Referring again to the drawings, FIG. 17 is a schematic diagramillustrating an embodiment for scanning and finding open space. Aplurality of nodes are in wireless or wired communication with asoftware defined radio, which receives information concerning openchannels following real-time scanning and access to external databasefrequency information.

FIG. 18 is a diagram of an embodiment of the invention wherein softwaredefined radio nodes are in wireless or wired communication with a mastertransmitter and device sensing master.

FIG. 19 is a process flow diagram of an embodiment method of temporallydividing up data into intervals for power usage analysis and comparison.The data intervals are initially set to seconds, minutes, hours, daysand weeks, but can be adjusted to account for varying time periods(e.g., if an overall interval of data is only a week, the data intervaldivisions would not be weeks). In one embodiment, the interval slicingof data is used to produce power variance information and reports.

FIG. 20 is a flow diagram illustrating an embodiment wherein frequencyto license matching occurs. In such an embodiment the center frequencyand bandwidth criteria can be checked against a database to check for alicense match. Both licensed and unlicensed bands can be checked againstthe frequencies, and, if necessary, non-correlating factors can bemarked when a frequency is uncorrelated.

FIG. 21 is a flow diagram illustrating an embodiment method forreporting power usage information, including locational data, databroken down by time intervals, frequency and power usage information perband, average power distribution, propagation models, atmosphericfactors, which is capable of being represented graphical,quantitatively, qualitatively, and overlaid onto a geographic ortopographic map.

FIG. 22 is a flow diagram illustrating an embodiment method for creatingfrequency arrays. For each initialization, an embodiment of theinvention will determine a center frequency, bandwidth, peak power,noise floor level, resolution bandwidth, power and date/time. Start andend frequencies are calculated using the bandwidth and center frequencyand like frequencies are aggregated and sorted in order to produce a setof frequency arrays matching power measurements captured in each band.

FIG. 23 is a flow diagram illustrating an embodiment method for reframeand aggregating power when producing frequency arrays.

FIG. 24 is a flow diagram illustrating an embodiment method of reportinglicense expirations by accessing static or FCC databases.

FIG. 25 is a flow diagram illustrating an embodiment method of reportingfrequency power use in graphical, chart, or report format, with theoption of adding frequencies from FCC or other databases.

FIG. 26 is a flow diagram illustrating an embodiment method ofconnecting devices. After acquiring a GPS location, static and FCCdatabases are accessed to update license information, if available. Afrequency scan will provide data to identify open spaces, detectinterferences and/or collisions. Based on the master device ID, set arandom generated token to select channel form available channel modeland continually transmit ID channel token. If node device reads ID, itwill set itself to channel based on token and device will connect tomaster device. Master device will then set frequency and bandwidthchannel. For each device connected to master, a frequency, bandwidth,and time slot in which to transmit is set. In one embodiment, thesesteps can be repeated until the max number of devices is connected. Asnew devices are connected, the device list is updated with channel modeland the device is set as active. Disconnected devices are set asinactive. If collision occurs, update channel model and get new tokenchannel. Active scans will search for new or lost devices and updatedevices list, channel model, and status accordingly. Channel model IDsare actively sent out for new or lost devices.

FIG. 27 is a flow diagram illustrating an embodiment method ofaddressing collisions.

FIG. 28 is a schematic diagram of an embodiment of the inventionillustrating a virtualized computing network and a plurality ofdistributed devices. FIG. 28 is a schematic diagram of one embodiment ofthe present invention, illustrating components of a cloud-basedcomputing system and network for distributed communication therewith bymobile communication devices. FIG. 28 illustrates an exemplaryvirtualized computing system for embodiments of the present inventionloyalty and rewards platform. As illustrated in FIG. 28, a basicschematic of some of the key components of a virtualized computing (orcloud-based) system according to the present invention are shown. Thesystem 2800 comprises at least one remote server computer 2810 with aprocessing unit 2811 and memory. The server 2810 is constructed,configured and coupled to enable communication over a network 2850. Theserver provides for user interconnection with the server over thenetwork with the at least one apparatus as described herein above 2840positioned remotely from the server. Apparatus 2840 includes a memory2846, a CPU 2844, an operating system 2847, a bus 2842, an input/outputmodule 2848, and an output or display 2849. Furthermore, the system isoperable for a multiplicity of devices or apparatus embodiments 2860,2870 for example, in a client/server architecture, as shown, each havingoutputs or displays 2869 and 2979, respectively. Alternatively,interconnection through the network 2850 using the at least one deviceor apparatus for measuring signal emitting devices, each of the at leastone apparatus is operable for network-based communication. Also,alternative architectures may be used instead of the client/serverarchitecture. For example, a computer communications network, or othersuitable architecture may be used. The network 2850 may be the Internet,an intranet, or any other network suitable for searching, obtaining,and/or using information and/or communications. The system of thepresent invention further includes an operating system 2812 installedand running on the at least one remote server 2810, enabling the server2810 to communicate through network 2850 with the remote, distributeddevices or apparatus embodiments as described herein above, the server2810 having a memory 2820. The operating system may be any operatingsystem known in the art that is suitable for network communication.

FIG. 29 shows a schematic diagram of aspects of the present invention.

FIG. 30 is a schematic diagram of an embodiment of the inventionillustrating a computer system, generally described as 3800, having anetwork 3810 and a plurality of computing devices 3820, 3830, 3840. Inone embodiment of the invention, the computer system 3800 includes acloud-based network 3810 for distributed communication via the network'swireless communication antenna 3812 and processing by a plurality ofmobile communication computing devices 3830. In another embodiment ofthe invention, the computer system 3800 is a virtualized computingsystem capable of executing any or all aspects of software and/orapplication components presented herein on the computing devices 3820,3830, 3840. In certain aspects, the computer system 3800 may beimplemented using hardware or a combination of software and hardware,either in a dedicated computing device, or integrated into anotherentity, or distributed across multiple entities or computing devices.

By way of example, and not limitation, the computing devices 3820, 3830,3840 are intended to represent various forms of digital devices 3820,3840 and mobile devices 3830, such as a server, blade server, mainframe,mobile phone, a personal digital assistant (PDA), a smart phone, adesktop computer, a netbook computer, a tablet computer, a workstation,a laptop, and other similar computing devices. The components shownhere, their connections and relationships, and their functions, aremeant to be exemplary only, and are not meant to limit implementationsof the invention described and/or claimed in this document.

In one embodiment, the computing device 3820 includes components such asa processor 3860, a system memory 3862 having a random access memory(RAM) 3864 and a read-only memory (ROM) 3866, and a system bus 3868 thatcouples the memory 3862 to the processor 3860. In another embodiment,the computing device 3830 may additionally include components such as astorage device 3890 for storing the operating system 3892 and one ormore application programs 3894, a network interface unit 3896, and/or aninput/output controller 3898. Each of the components may be coupled toeach other through at least one bus 3868. The input/output controller3898 may receive and process input from, or provide output to, a numberof other devices 3899, including, but not limited to, alphanumeric inputdevices, mice, electronic styluses, display units, touch screens, signalgeneration devices (e.g., speakers) or printers.

By way of example, and not limitation, the processor 3860 may be ageneral-purpose microprocessor (e.g., a central processing unit (CPU)),a graphics processing unit (GPU), a microcontroller, a Digital SignalProcessor (DSP), an Application Specific Integrated Circuit (ASIC), aField Programmable Gate Array (FPGA), a Programmable Logic Device (PLD),a controller, a state machine, gated or transistor logic, discretehardware components, or any other suitable entity or combinationsthereof that can perform calculations, process instructions forexecution, and/or other manipulations of information.

In another implementation, shown in FIG. 30, a computing device 3840 mayuse multiple processors 3860 and/or multiple buses 3868, as appropriate,along with multiple memories 3862 of multiple types (e.g., a combinationof a DSP and a microprocessor, a plurality of microprocessors, one ormore microprocessors in conjunction with a DSP core).

Also, multiple computing devices may be connected, with each deviceproviding portions of the necessary operations (e.g., a server bank, agroup of blade servers, or a multi-processor system). Alternatively,some steps or methods may be performed by circuitry that is specific toa given function.

According to various embodiments, the computer system 3800 may operatein a networked environment using logical connections to local and/orremote computing devices 3820, 3830, 3840 through a network 3810. Acomputing device 3830 may connect to a network 3810 through a networkinterface unit 3896 connected to the bus 3868. Computing devices maycommunicate communication media through wired networks, direct-wiredconnections or wirelessly such as acoustic, RF or infrared through awireless communication antenna 3897 in communication with the network'swireless communication antenna 3812 and the network interface unit 3896,which may include digital signal processing circuitry when necessary.The network interface unit 3896 may provide for communications undervarious modes or protocols.

In one or more exemplary aspects, the instructions may be implemented inhardware, software, firmware, or any combinations thereof. A computerreadable medium may provide volatile or non-volatile storage for one ormore sets of instructions, such as operating systems, data structures,program modules, applications or other data embodying any one or more ofthe methodologies or functions described herein. The computer readablemedium may include the memory 3862, the processor 3860, and/or thestorage device 3890 and may be a single medium or multiple media (e.g.,a centralized or distributed computer system) that store the one or moresets of instructions 3900. Non-transitory computer readable mediaincludes all computer readable media, with the sole exception being atransitory, propagating signal per se. The instructions 3900 may furtherbe transmitted or received over the network 3810 via the networkinterface unit 3896 as communication media, which may include amodulated data signal such as a carrier wave or other transportmechanism and includes any delivery media. The term “modulated datasignal” means a signal that has one or more of its characteristicschanged or set in a manner as to encode information in the signal.

Storage devices 3890 and memory 3862 include, but are not limited to,volatile and non-volatile media such as cache, RAM, ROM, EPROM, EEPROM,FLASH memory or other solid state memory technology, disks or discs(e.g., digital versatile disks (DVD), HD-DVD, BLU-RAY, compact disc(CD), CD-ROM, floppy disc) or other optical storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or any other medium that can be used to store the computer readableinstructions and which can be accessed by the computer system 3800.

It is also contemplated that the computer system 3800 may not includeall of the components shown in FIG. 30, may include other componentsthat are not explicitly shown in FIG. 30, or may utilize an architecturecompletely different than that shown in FIG. 30. The variousillustrative logical blocks, modules, elements, circuits, and algorithmsdescribed in connection with the embodiments disclosed herein may beimplemented as electronic hardware, computer software, or combinationsof both. To clearly illustrate this interchangeability of hardware andsoftware, various illustrative components, blocks, modules, circuits,and steps have been described above generally in terms of theirfunctionality. Whether such functionality is implemented as hardware orsoftware depends upon the particular application and design constraintsimposed on the overall system. Skilled artisans may implement thedescribed functionality in varying ways for each particular application(e.g., arranged in a different order or partitioned in a different way),but such implementation decisions should not be interpreted as causing adeparture from the scope of the present invention.

The present invention further provides for aggregating data from atleast two apparatus units by at least one server computer and storingthe aggregated data in a database and/or in at least one database in acloud-based computing environment or virtualized computing environment,as illustrated in FIG. 28 or FIG. 30. The present invention furtherprovides for remote access to the aggregated data and/or data from anyof the at least one apparatus unit, by distributed remote user(s) fromcorresponding distributed remote device(s), such as by way of exampleand not limitation, desktop computers, laptop computers, tabletcomputers, mobile computers with wireless communication operations,smartphones, mobile communications devices, and combinations thereof.The remote access to data is provided by software applications operableon computers directly (as a “desktop” application) and/or as a webservice that allows user interface to the data through a secure,network-based website access.

In other embodiments of the present invention, which include the baseinvention described hereinabove, and further including the functions ofmachine “learning”, modulation detection, automatic signal detection(ASD), FFT replay, and combinations thereof.

Automatic modulation detection and machine “learning” includes automaticsignal variance determination by at least one of the following methods:date and time from location set, and remote access to the apparatus unitto determine variance from different locations and times, in addition tothe descriptions of automatic signal detection and thresholddetermination and setting. Environments vary, especially where there aremany signals, noise, interference, variance, etc., so tracking signalsautomatically is difficult, and a longstanding, unmet need in the priorart. The present invention provides for automatic signal detection usinga sample of measured and sensed data associated with signals over timeusing the at least one apparatus unit of the present invention toprovide an automatically adjustable and adaptable system. For eachspectrum scan, the data is automatically subdivided into “windows”,which are sections or groups of data within a frequency space. Real-timeprocessing of the measured and sensed data on the apparatus unit(s) ordevices combined with the windowing effect provides for automaticcomparison of signal versus noise within the window to provide for noiseapproximation, wherein both signals and noise are measured and sensed,recorded, analyzed compared with historical data to identify and outputsignals in a high noise environment. It is adaptive and iterative toinclude focused windows and changes in the window or frequency rangesgrouped. The resulting values for all data are squared in the analysis,which results in signals identified easily by the apparatus unit ashaving significantly larger power values compared with noise; additionalanalytics provide for selection of the highest power value signals andreview of the original data corresponding thereto. Thus, the at leastone apparatus automatically determines and identifies signals comparedto noise in the RF spectrum.

The apparatus unit or device of the present invention further includes atemporal anomaly detector (or “learning channel”). The first screen shotillustrated in FIG. 31 shows the blank screen, the second screen shotillustrated in FIG. 32 shows several channels that the system has“learned”. This table can be saved to disk as a spreadsheet and reusedon subsequent surveys at the same location. The third screen shot shownin FIG. 33 displays the results when run with the “Enable OOB Signals”button enabled. In this context OOB means “Out Of Band” or rogue orpreviously unidentified signals. Once a baseline set of signals has beenlearned by the system, it can be used with automatic signal detection toclearly show new, unknown signals that were not present when the initiallearning was done as shown in FIG. 34.

In a similar capacity, the user can load a spreadsheet that they haveconstructed on their own to describe the channels that they expect tosee in a given environment, as illustrated in FIG. 34. When run with OOBdetection, the screen shot shows the detection of signals that were notin the user configuration. These rogue signals could be a possiblesource of interference, and automatic detection of them can greatlyassist the job of an RF Manager.

FIGS. 31-34 illustrate the functions and features of the presentinvention for automatic or machine “learning” as described hereinabove.

Automatic signal detection of the present invention eliminates the needfor a manual setting of a power threshold line or bar, as with the priorart. The present invention does not require a manual setting of powerthreshold bar or flat line to identify signals instead of noise, insteadit uses information learned directly from the changing RF environment toidentify signals. Thus, the apparatus unit or device may be activatedand left unattended to collect data continuously without the need formanual interaction with the device directly. Furthermore, the presentinvention allows remote viewing of live data in real time on a displayof a computer or communications device in network-based connection butremotely positioned from the apparatus unit or device, and/or remoteaccess to device settings, controls, data, and combinations thereof Thenetwork-based communication may be selected from mobile, satellite,Ethernet, and functional equivalents or improvements with securityincluding firewalls, encryption of data, and combinations thereof.

Regarding FFT replay, the present invention apparatus units are operableto replay data and to review and/or replay data saved based upon anunknown event, such as for example and not limitation, reported alarmsand/or unique events, wherein the FFT replay is operable to replaystored sensed and measured data to the section of data nearest thereported alarm and/or unique event. By contrast, prior art provides forrecording signals on RF spectrum measurement devices, which transmit orsend the raw data to an external computer for analysis, so then it isimpossible to replay or review specific sections of data, as they arenot searchable, tagged, or otherwise sectioned into subgroups of data orstored on the device.

Automatic Signal Detection

The previous approach to ASD was to subtract a calibration vector fromeach FFT sample set (de-bias), then square each resulting value and lookfor concentrations of energy that would differentiate a signal fromrandom baseline noise. The advantages of this approach are that, by theuse of the calibration vector (which was created using the receiveritself with no antenna), variations in the baseline noise that are dueto the characteristics of the receiver, front end filtering, attenuationand A/D converter hardware can be closed tracked. On most modernequipment, the designers take steps to keep the overall response flat,but there are those that do not. FIG. 35 is an example of a receiverthat has marked variations on baseline behavior across a wide spectrum(9 MHz-6 GHz).

The drawbacks to this approach are: 1) It requires the use of several“tuning” variables which often require the user to adjust and fiddlewith in order to achieve good signal recognition. A fully automaticsignal detection system should be able to choose values for theseparameters without the intervention of an operator. 2) It does not takeinto account variations in the baseline noise floor that are introducedby RF energy in a live environment. Since these variations were notpresent during calibration, they are not part of the calibration vectorand cannot be “canceled out” during the de-bias phase. Instead theyremain during the square and detect phase, often being mistakenlyclassified as signal. An example of this is FIG. 36, a normal spectrumfrom 700 MHz to 790 MHz. The threshold line (baby blue) indicates thelevel where signal can be differentiated from noise. FIG. 37 illustratesthe same spectrum at a different time where an immensely powerful signalat about 785 MHz has caused undulations in the noise floor all the waydown to 755 MHz. It is clear to see by the placement of the thresholdline large blocks of the noise are now going to be recognized as signal.Not only are the 4 narrow band signals now going to be mistakenly seenas one large signal, there is an additional lump of noise around 760 MHzthat represents no signal at all, but will be classified as such.

In order to solve these two problems, and provide a fully automaticsignal detection system, a new approach has been taken to prepare thecalibration vector. The existing square and detect algorithm works wellif the data are de-biased properly with a cleverly chosen calibrationvector, it's just that the way the calibration vector was created wasnot sufficient.

FIG. 38 illustrates a spectrum from 1.9 GHz to 2.0 GHz, along with someadditional lines that indicate the functions of the new algorithm. Line1 (brown) at the bottom displays the existing calibration vector createdby running the receiver with no antenna. It is clear to see that, ifused as is, it is too low to be used to de-bias the data shown as line 2(dark blue). Also, much of the elevations in noise floor will wind upbeing part of the signals that are detected. In order to compensate forthis, the user was given a control (called “Bias”) that allowed them toraise or lower the calibration vector to hopefully achieve a morereasonable result. But, as illustrated in FIG. 37, no adjustment willsuffice when the noise floor has been distorted due to the injection oflarge amounts of energy.

So, rather than attempt to make the calibration vector fit the data, thenew approach examines the data itself in an attempt to use parts of itas the correction vector. Line 3 (light purple) in FIG. 38 is the resultof using a 60-sample smoothing filter to average the raw data. Itclearly follows the data, but it removes the “jumpiness”. This can bebetter seen in FIG. 39 which is a closeup view of the first part of theoverall spectrum. The difference between the smoothed data shown as line3 (light purple) and the original data shown as line 2 (dark blue) isdisplayed clearly.

The new Gradient Detection algorithm is applied to the smoothed data todetect locations where the slope of the line changes quickly. In placeswhere the slope changes quickly in a positive direction, the algorithmmarks the start of a signal. On the other side of the signal thegradient again changes quickly to become more horizontal. At that pointthe algorithm determines it is the end of a signal. A second smoothingpass is performed on the smoothed data, but this time, those values thatfall between the proposed start and end of signal are left out of theaverage. The result is line 4 (baby blue) in FIGS. 38 and 39, which isthen used as the new calibration vector. This new calibration vectorshown as ling 4 (baby blue) is then used to de-bias the raw data whichis then passed to the existing square and detect ASD algorithm.

One of the other user-tunable parameters in the existing ASD system wascalled “Sensitivity”. This was a parameter that essentially set athreshold of energy, above which each FFT bin in a block of binsaveraged together must exceed in order for that block of bins to beconsidered a signal. In this way, rather than a single horizontal lineto divide signal from noise, each signal can be evaluated individually,based on its average power. The effect of setting this value too low wasthat tiny fluctuations of energy that are actually noise would sometimesappear to be signals. Setting the value too high would result in thealgorithm missing a signal. In order to automatically choose a value forthis parameter, the new system uses a “Quality of Service” feedback fromthe Event Compositor, a module that processes the real-time events fromthe ASD system and writes signal observations into a database. When thesensitivity value is too low, the random bits of energy that ASDmistakenly sees as signal are very transient. This is due to the randomnature of noise. The Event Compositor has a parameter called a“Pre-Recognition Delay” that sets the minimum number of consecutivescans that it must see a signal in order for it to be considered acandidate for a signal observation database entry (in order to catchlarge fast signals, an exception is made for large transients that areeither high in peak power, or in bandwidth). Since the randomfluctuations seldom persist for more than 1 or 2 sweeps, the EventCompositor ignores them, essentially filtering them out. If there are alarge number of these transients, the Event Compositor provides feedbackto the ASD module to inform it that its sensitivity is too low.Likewise, if there are no transients at all, the feedback indicates thesensitivity is too high. Eventually, the system arrives at an optimalsetting for the sensitivity parameter.

The result is a fully automated signal detection system that requires nouser intervention or adjustment. The black brackets at the top of FIG.38 illustrate the signals recognized by the system, clearly indicatingits accuracy.

Because the system relies heavily upon averaging, a new algorithm wascreated that performs an N sample average in fixed time; i.e. regardlessof the width of the average, N, each bin requires 1 addition, 1subtraction, and 1 division. A simpler algorithm would require Nadditions and 1 division per bin of data. A snippet of the code isprobably the best description:

public double [ ] smoothingFilter( double [ ] dataSet, int filterSize ){ double [ ] resultSet = new double[ dataSet.length ]; double temp =0.0; int i=0; int halfSize = filterSize/2; for( i=0 ; i < filterSize ;i++ ) {  temp += dataSet[i]; // load accumulator with the first N/2values.  if( i < halfSize ) resultSet[i] = dataSet[i]; } for( i=halfSize; i < (dataSet.length − halfSize) ; i++ ) {  resultSet[i] = temp /filterSize; // Compute the average and store it  temp −= dataSet[i−halfSize ]; // take out the oldest value  temp += dataSet[ i+halfSize]; // add in the newest value } while( i < dataSet.length ) { resultSet[i] = dataSet[i];  i++; } return( resultSet );  }

Automatic Signal Detection (ASD) with Temporal Feature Extraction (TFE)

The system in the present invention uses statistical learning techniquesto observe and learn an RF environment over time and identify temporalfeatures of the RF environment (e.g., signals) during a learning period.

A knowledge map is formed based on learning data from a learning period.In one embodiment, the knowledge map is a spectrum map displayingspectrum usage data in the RF environment. Real-time signal events aredetected by an ASD system and scrubbed against the knowledge map todetermine if the real-time signal events are typical and expected forthe environment, or if there is any event not typical nor expected.

The knowledge map consists of an array of normal distributions, whereeach distribution column is for each frequency bin of the FFT result setprovided by a software defined radio (SDR). Each vertical columncorresponds to a bell-shaped curve for that frequency. Each pixelrepresents a count of how many times that frequency was seen at thatpower level.

A learning routine takes power levels of each frequency bin, uses thepower levels as an index into each distribution column corresponding toeach frequency bin, and increments the counter in a locationcorresponding to a power level.

FIG. 40 illustrates a knowledge map obtained by a TFE process. The topwindow shows the result of real-time spectrum sweep of an environment.The bottom window shows a knowledge map, which color codes (e.g., black,dark blue, light blue) the values in each column (normal distribution)based on how often the power level of that frequency (column) has beenat a particular level.

The TFE function monitors its operation and produces a “settledpercent.” The settled percent is the percentage of the values of theincoming FFT result set that the system has seen before. In this way,the system can know if it is ready to interpret the statistical datathat it has obtained. Once it reaches a point where most of the FFTvalues have been seen before (99.95% or better), it can then perform aninterpretation operation.

FIG. 41 illustrates an interpretation operation based on a knowledgemap. Similar to FIG. 40, the bottom of FIG. 41 displays a knowledge map,which color codes (e.g., black, dark blue, baby blue) the values in eachcolumn (normal distribution) based on how often the power level of thatfrequency (column) has been at a particular level. During theinterpretation operation, the system extracts valuable signalidentification from the knowledge map. Some statistical quantities areidentified. For each column, the power level at which a frequency isseen the most is determined (peak of the distribution curve), which isrepresented by line a (red) in FIG. 41. A desired percentage of powerlevel values is located between the high and low boundaries of the powerlevels (shoulders of the curve), which are represented by lines b(white) in FIG. 41. The desired percentage is adjustable. In FIG. 41,the desired percentage is set at 42% based on the learning data. In oneembodiment, a statistical method is used to obtain a desirablepercentage that provides the highest degree of “smoothness”—lowestdeviation from column to column. Then, a profile is drawn based on thelearning data, which represents the highest power level at which eachfrequency has been seen during learning. In FIG. 41, the profile isrepresented by line c (green).

Gradient detection is then applied to the profile to identify areas oftransition. An algorithm continues to accumulate a gradient value aslong as the “step” from the previous cell to this cell is alwaysnon-zero and the same direction. When it arrives at a zero or differentdirection step, it evaluates the accumulated difference to see if it issignificant, and if so, considers it a gradient. A transition isidentified by a continuous change (from left to right) that exceeds theaverage range between the high and low boundaries of power levels shownas line b (white) in FIG. 41. Positive and negative gradients arematched, and the resulting interval is identified as a signal. FIG. 42shows the identification of signals, which are represented by the blackbrackets above the knowledge display. Similar to FIG. 41, the knowledgemap in FIG. 42 color codes (e.g., black, dark blue, baby blue) thevalues in each column (normal distribution) based on how often the powerlevel of that frequency (column) has been at a particular level. Lines b(white) represents the high and low boundaries of a desirable percentageof power level. Line c (green) represents a profile of the RFenvironment comprising the highest power level at which each frequencyhas been seen during learning.

FIG. 43 shows more details of the narrow band signals at the left of thespectrum around 400 MHz in FIG. 42. Similar to FIG. 41, the knowledgemap in FIG. 43 color codes (e.g., black, dark blue, baby blue) thevalues in each column (normal distribution) based on how often the powerlevel of that frequency (column) has been at a particular level. Lines b(white) represents the high and low boundaries of a desirable percentageof power level. Line c (green) represents a profile of the RFenvironment comprising the highest power level at which each frequencyhas been seen during learning. The red cursor at 410.365 MHz in FIG. 43points to a narrow band signal. The real-time spectrum sweep on the topwindow shows the narrow band signal, and the TFE process identifies thenarrow band signal as well.

To a prior art receiver, the narrow band signal hidden within a widebandsignal is not distinguishable or detectable. The systems and methods anddevices of the present invention are operable to scan a wideband withhigh resolution or high definition to identify channel divisions withina wideband, and identify narrowband signals hidden within the widebandsignal, which are not a part of the wideband signal itself, i.e., thenarrow band signals are not part of the bundled channels within thewideband signal.

FIG. 44 shows more details of the two wide band signals around 750 MHzand a similar signal starting at 779 MHz. The present invention detectsthe most prominent parts of the signal starting at 779 MHz. Thetransmitters of these two wide band signals are actually in thedistance, and normal signal detectors, which usually have a fixedthreshold, are not able to pick up these two wide band signals but onlysee them as static noises. Because the TFE system in the presentinvention uses an aggregation of signal data over time, it can identifythese signals and fine tune the ASD sensitivity of individual segments.Thus, the system in the present invention is able to detect signals thatnormal radio gear cannot. ASD in the present invention, is enhanced bythe knowledge obtained by TFE and is now able to detect and record thesesignals where gradient detection alone would not have seen them. Thethreshold bar in the present invention is not fixed, but changeable.

Also, at the red cursor in FIG. 44 is a narrow band signal in thedistance that normally would not be detected because of its low power atthe point of observation. But, the present invention interpretsknowledge gained over time and is able to identify that signal.

FIG. 45 illustrates the operation of the ASD in the present invention.Line A (green) shows the spectrum data between 720 MHz and 791 MHz.1^(st) and 2^(nd) derivatives of the power levels are calculated insidespectrum on a cell by cell basis, displayed as the overlapping line B(blue) and line C (red) at the top. The algorithm then picks the mostprominent derivatives and performs a squaring function on them asdisplayed by line D (red). The software then matches positive andnegative gradients, to identify the edges of the signals, which arerepresented by the brackets on the top. Two wideband signals areidentified, which may be CDMA, LTE, or other communication protocol usedby mobile phones. Line E (red) at the bottom is a baseline establishedby averaging the spectrum and removing areas identified by thegradients. At the two wideband signals, line E (red) is flat. Bysubtracting the baseline from the real spectrum data, groups of cellswith average power above baseline are identified, and the averagingalgorithm is run against those areas to apply the sensitivitymeasurement.

The ASD system has the ability to distinguish between large eruptions ofenergy that increase the baseline noise and the narrow band signals thatcould normally be swamped by the additional energy because it generatesits baseline from the spectrum itself and looks for relative gradientsrather than absolute power levels. This baseline is then subtracted fromthe original spectrum data, revealing the signals, as displayed by thebrackets at the top of the screen. Note that the narrow-band signals arestill being detected (tiny brackets at the top that look more like dots)even though there is a hump of noise super-imposed on them.

TFE is a learning process that augments the ASD feature in the presentinvention. The ASD system enhanced with TFE function in the presentinvention can automatically tune parameters based on a segmented basis,the sensitivity within an area is changeable. The TFE processaccumulates small differences over time and signals become more and moreapparent. In one embodiment, the TFE takes 40 samples per second over a5-minute interval. The ASD system in the present invention is capable ofdistinguishing signals based on gradients from a complex and movingnoise floor without a fixed threshold bar when collecting data from anenvironment.

The ASD system with TFE function in the present invention is unmannedand water resistant. It runs automatically 24/7, even submerged inwater.

The TFE is also capable of detecting interferences and intrusions. Inthe normal environment, the TFE settles, interprets and identifiessignals. Because it has a statistical knowledge of the RF landscape, itcan tell the difference between a low power, wide band signal that itnormally sees and a new higher power narrow band signal that may be anintruder. This is because it “scrubs” each of the FFT bins of each eventthat the ASD system detects against its knowledge base. When it detectsthat a particular group of bins in a signal from ASD falls outside thestatistical range that those frequencies normally are observed, thesystem can raise an anomaly report. The TFE is capable of learning newknowledge, which is never seen before, from the signals identified by anormal detector. In one embodiment, a narrow band signal (e.g., a pitcrew to car wireless signal) impinges on an LTE wideband signal, thenarrow band signal may be right beside the wideband signal, or drift inand out of the wideband signal. On display, it just looks like an LTEwideband signal. For example, a narrow band signal with a bandwidth of12 kHz or 25-30 kHz in a wideband signal with a bandwidth of 5 MHz overa 6 GHz spectrum just looks like a spike buried in the middle. But,because signals are characterized in real time against learnedknowledge, the proposed ASD system with TFE function is able to pick outnarrow band intruder immediately.

The present invention is able to detect a narrow band signal with abandwidth from 1-2 kHz to 60 kHz inside a wideband signal (e.g., with abandwidth of 5 MHz) across a 6 GHz spectrum. In FIGS. 40-45, thefrequency resolution is 19.5 kHz, and a narrow band signal with abandwidth of 2-3 kHz can be detected. The frequency resolution is basedon the setting of the FFT result bin size.

Statistical learning techniques are used for extracting temporalfeature, creating a statistical knowledge map of what each frequency isand determining variations and thresholds and etc. The ASD system withTFE function in the present invention is capable of identifying,demodulating and decoding signals, both wideband and narrowband withhigh energy.

If a narrowband signal is close to the end of wideband LTE signal, thewideband LTE signal is distorted at the edge. If multiple narrowbandsignals are within a wideband signal, the top edge of the widebandsignal is ragged as the narrow band signal is hidden within the wideband signal. If one narrow band signal is in the middle of a widebandsignal, the narrow band signal is usually interpreted as a cell withinthe wideband signal. However, the ASD system with TFE function in thepresent invention learns power levels in a spectrum section over time,and is able to recognize the narrow band signal immediately.

The present invention is operable to log the result, display one achannel screen, notify operator and send alarms, etc. The presentinvention auto records spectrum, but does not record all the time. Whena problem is identified, relevant information is auto recorded in highdefinition. In one embodiment, the ASD device automatically andcontinuously stores and records all monitored spectral data. In oneembodiment, the ASD device stores the data for multiple days or multipleyears.

The ASD system with TFE in the present invention is used for spectrummanagement. The system in the present invention is set up in a normalenvironment and starts learning and stores at least one learning map init. The learning function of the ASD system in the present invention canbe enabled and disabled. When the ASD system is exposed to a stableenvironment and has learned what is normal in the environment, it willstop its learning process. The environment is periodically reevaluated.The learning map is updated at a predetermined timeframe. After aproblem is detected, the learning map will also be updated.

The ASD system in the present invention can be deployed in stadiums,ports, airports, or on borders. In one embodiment, the ASD system learnsand stores the knowledge in that environment. In another embodiment, theASD system downloads prior knowledge and immediately displays it. Inanother embodiment, an ASD device can learn from other ASD devicesglobally.

In operation, the ASD system then collects real time data and comparesto the learning map stored for signal identification. Signals identifiedby the ASD system with TFE function may be determined to be an error byan operator. In that situation, an operator can manually edit or erasethe error, essentially “coaching” the learning system.

The systems and devices in the present invention create a channel planbased on user input, or external databases, and look for signals thatare not there. Temporal Feature Extraction not only can define a channelplan based on what it learns from the environment, but it also “scrubs”each spectrum pass against the knowledge it has learned. This allows itto not only identify signals that violate a prescribed channel plan, butit can also discern the difference between a current signal, and thesignal that it has previously seen in that frequency location. If thereis a narrow band interference signal where there typically is a wideband signal, the system will identify it as an anomaly because it doesnot match the pattern of what is usually in that space.

The device in the present invention is designed to be autonomous. Itlearns from the environment, and, without operator intervention, candetect anomalous signals that either were not there before, or havechanged in power or bandwidth. Once detected, the device can send alertsby text or email and begin high resolution spectrum capture, or IQcapture of the signal of interest.

FIG. 40 illustrates an environment in which the device is learning.There are some obvious signals, but there is also a very low level wideband signal between 746 MHz and 755 MHz. Typical threshold-orientedsystems would not catch this. But, the TFE system takes a broader viewover time. The signal does not have to be there all the time or bepronounced to be detected by the system. Each time it appears in thespectrum serves to reinforce the impression on the learning fabric.These impressions are then interpreted and characterized as signals.

FIG. 43 shows the knowledge map that the device has acquired during itslearning system, and shows brackets above what it has determined aresignals. Note that the device has determined these signals on its ownwithout any user intervention, or any input from any databases. It is asimple thing to then further categorize the signals by matching againstdatabases, but what sets the device in the present invention apart isthat, like its human counterpart, it has the ability to draw its ownconclusions based on what it has seen.

FIG. 44 shows a signal identified by the device in the present inventionbetween 746 MHz and 755 MHz with low power levels. It is clear to seethat, although the signal is barely distinguishable from the backgroundnoise, TFE clearly has identified its edges. Over to the far right is asimilar signal that is further away so that it only presents traces ofitself. But again, because the device in the present invention istrained to distinguish random and coherent energy patterns over time, itcan clearly pick out the pattern of a signal. Just to the left of thatfaint signal was a transient narrow band signal at 777.653 MHz. Thissignal is only present for a brief period of time during the training,typically 0.5-0.7 seconds each instance, separated by minutes ofsilence, yet the device does not miss it, remembers those instances andcategorizes them as a narrow band signal.

The identification and classification algorithms that the system uses toidentify Temporal Features are optimized to be used in real time. Noticethat, even though only fragments of the low level wide band signal aredetected on each sweep, the system still matches them with the signalthat it had identified during its learning phase.

Also as the system is running, it is scrubbing each spectral sweepagainst its knowledge map. When it finds coherent bundles of energy thatare either in places that are usually quiet, or have higher power orbandwidth than it has seen before, it can automatically send up a redflag. Since the system is doing this in Real Time, it has criticalrelevance to those in harm's way—the first responder, or the war fighterwho absolutely must have clear channels of communication or instantsituational awareness of eminent threats. It's one thing to geolocate asignal that the user has identified. It's an entirely differentdimension when the system can identify the signal on its own before theuser even realizes it's there. Because the device in the presentinvention can pick out these signals with a sensitivity that is farsuperior to a simple threshold system, the threat does not have topresent an obvious presence to be detected and alerted.

Devices in prior art merely make it easy for a person to analyzespectral data, both in real time and historically, locally or remotely.But the device in the present invention operates as an extension of theperson, performing the learning and analysis on its own, and evenfinding things that a human typically may miss.

The device in the present invention can easily capture signalidentifications, match them to databases, store and upload historicaldata. Moreover, the device has intelligence and the ability to be morethan a simple data storage and retrieval device. The device is awatchful eye in an RF environment, and a partner to an operator who istrying to manage, analyze, understand and operate in the RF environment.

Geolocation

The prior art is dependent upon a synchronized receiver for power,phase, frequency, angle, and time of arrival, and an accurate clock fortiming, and significantly, requires three devices to be used, whereinall are synchronized and include directional antennae to identify asignal with the highest power. Advantageously, the present inventiondoes not require synchronization of receivers in a multiplicity ofdevices to provide geolocation of at least one apparatus unit or deviceor at least one signal, thereby reducing cost and improvingfunctionality of each of the at least one apparatus in the systemsdescribed hereinabove for the present invention. Also, the presentinvention provides for larger frequency range analysis, and providesdatabase(s) for capturing events, patterns, times, power, phase,frequency, angle, and combinations for the at least one signal ofinterest in the RF spectrum. The present invention provides for bettermeasurements and data of signal(s) with respect to time, frequency withrespect to time, power with respect to time, geolocation, andcombinations thereof. In preferred embodiments of the at least oneapparatus unit of the present invention, geolocation is providedautomatically by the apparatus unit using at least one anchor pointembedded within the system, by power measurements and transmission thatprovide for “known” environments of data. The known environments of datainclude measurements from the at least one anchorpoint that characterizethe RF receiver of the apparatus unit or device. The known environmentsof data include a database including information from the FCC databaseand/or user-defined database, wherein the information from the FCCdatabase includes at least maximum power based upon frequency, protocol,device type, and combinations thereof. With the geolocation function ofthe present invention, there is no requirement to synchronize receiversas with the prior art; the at least one anchorpoint and location of anapparatus unit provide the required information to automatically adjustto a first anchorpoint or to a second anchorpoint in the case of atleast two anchorpoints, if the second anchorpoint is easier to adopt.The known environment data provide for expected spectrum and signalbehavior as the reference point for the geolocation. Each apparatus unitor device includes at least one receiver for receiving RF spectrum andlocation information as described hereinabove. In the case of onereceiver, it is operable with and switchable between antennae forreceiving RF spectrum data and location data; in the case of tworeceivers, preferably each of the two receivers are housed within theapparatus unit or device. A frequency lock loop is used to determine ifa signal is moving, by determining if there is a Doppler change forsignals detected.

Location determination for geolocation is provided by determining apoint (x, y) or Lat Lon from the at least three anchor locations (x1,y1); (x2, y2); (x3, y3) and signal measurements at either of the node oranchors. Signal measurements provide a system of non-linear equationsthat must be solved for (x, y) mathematically; and the measurementsprovide a set of geometric shapes which intersect at the node locationfor providing determination of the node.

For trilateration methods for providing observations to distances thefollowing methods are used:

${R\; S\; S} = {d = {d_{0}10\left( \frac{P_{0} - P_{r}}{10n} \right)}}$

wherein d_(o) is the reference distance derived from the referencetransmitter and signal characteristics (e.g., frequency, power,duration, bandwidth, etc.); P_(o) is the power received at the referencedistance; P_(r) is the observed received power; and n is the path lossexponent; and Distance from observations is related to the positions bythe following equations:

$d_{1} = \left( \sqrt{\left( {x - x_{1}} \right)^{2} + \left( {y - y_{1}} \right)^{2}} \right)$$d_{2} = \left( \sqrt{\left( {x - x_{2}} \right)^{2} + \left( {y - y_{2}} \right)^{2}} \right)$$d_{3} = \left( \sqrt{\left( {x - x_{3}} \right)^{2} + \left( {y - y_{3}} \right)^{2}} \right)$

Also, in another embodiment of the present invention, a geolocationapplication software operable on a computer device or on a mobilecommunications device, such as by way of example and not limitation, asmartphone, is provided. Method steps are illustrated in the flowdiagram shown in FIG. 46, including starting a geolocation app; callingactive devices via a connection broker; opening spectrum displayapplication; selecting at least one signal to geolocate; selecting atleast three devices (or apparatus unit of the present invention) withina location or region, verifying that the devices or apparatus units aresynchronized to a receiver to be geolocated; perform signal detection(as described hereinabove) and include center frequency, bandwidth, peakpower, channel power, and duration; identify modulation of protocoltype, obtain maximum, median, minimum and expected power; calculatingdistance based on selected propagation model; calculating distance basedon one (1) meter path loss; calculating distance based on one (1) meterpath loss model; calculating distance based on one (1) meter path lossmodel; perform circle transformations for each location; checking if RFpropagation distances form circles that are fully enclosed; checking ifRF propagation form circles that do not intersect; performingtrilateration of devices; deriving z component to convert back to knownGPS Lat Lon (latitude and longitude) coordinate; and making coordinatesand set point as emitter location on mapping software to indicate thegeolocation.

The equations referenced in FIG. 46 are provided hereinbelow:

Equation 1 for calculating distance based on selected propagation model:

PLossExponent=(Parameter C−6.55*log 10(BS_AntHeight))/10

MS_AntGainFunc=3.2*(log 10(11.75*MS_AntHeight))²−4.97

Constant(C)=ParameterA+ParameterB*log 10(Frequency)−13.82*log10(BS_AntHeight)−MS_AntGainFunc

DistanceRange=10^(((PLoss−PLossConstant)/10*PLossExponent)))

Equation 2 for calculating distance based on 1 meter Path Loss Model(first device):

d ₀=1;k=PLossExponent;PL_d=Pt+Gt−RSSI−TotalMargin

PL_0=32.44+10*k log 10(d ₀)+10*k*log 10(Frequency)

D=d ₀*(10^(((PL_dPL_0)/(10k))))

Equation 3: (same as equation 2) for second device

Equation 4: (same as equation 2) for third device

Equation 5: Perform circle transformations for each location (x, y, z)Distance d; Verify A^(T)A=0; where A={matrix of locations 1−N} inrelation to distance; if not, then perform circle transformation check

Equation 6: Perform trilateration of devices if more than three (3)devices aggregation and trilaterate by device; set circles to zeroorigin and solve from y=Ax where y=[x, y] locations

Equation 7:

$\begin{bmatrix}x \\y\end{bmatrix} = {\begin{bmatrix}{2\left( {x_{a} - x_{c}} \right)} & {2\left( {y_{a} - y_{c}} \right)} \\{2\left( {x_{b} - x_{c}} \right)} & {2\left( {y_{b} - y_{c}} \right)}\end{bmatrix}^{- 1}\begin{bmatrix}{x_{a}^{2} - x_{c}^{2} + y_{a}^{2} - y_{c}^{2} + d_{c}^{2} - d_{a}^{2}} \\{x_{b}^{2} - x_{c}^{2} + y_{b}^{2} - y_{c}^{2} + d_{c}^{2} - d_{b}^{2}}\end{bmatrix}}$

Note that check if RF propagation distances form circles where one ormore circles are Fully Enclosed if it is based upon Mod Type and PowerMeasured, then Set Distance 1 of enclosed circle to Distance 2 minus thedistance between the two points. Also, next, check to see if some of theRF Propagation Distances Form Circles, if they d₀ not intersect, then ifso based on Mod type and Max RF power Set Distance to each circle toDistance of Circle+(Distance between circle points−Sum of theDistances)/2 is used. Note that deriving z component to convert back toknown GPS lat lon coordinate is provided by: z=sqrt(Dist²−x²−y²).

Accounting for unknowns using Differential Received Signal Strength(DRSS) is provided by the following equation when reference or transmitpower is unknown:

$\frac{d_{i}}{d_{j}} = 10^{(\frac{R_{r_{j}} - P_{r_{i}}}{10n})}$

And where signal strength measurements in dBm are provided by thefollowing:

${{P_{{r\;}_{2}}\left( {{dB}\; m} \right)} - {P_{r_{1}}\left( {{dB}\; m} \right)}} = {{{10}\; n\; {\log \left( \sqrt{\left( {x - x_{1}} \right)^{2} + \left( {y - y_{1}} \right)^{2}} \right)}} - {10n\; \log_{10}\; \left( \sqrt{\left( {x - x_{2}} \right)^{2} + \left( {y - y_{2}} \right)^{2}} \right)}}$${{P_{r_{3}}\left( {{dB}\; m} \right)} - {P_{r_{1}}\left( {{dB}\; m} \right)}} = {{10n\; {\log_{10}\left( \sqrt{\left( {x - x_{1}} \right)^{2} + \left( {y - y_{1}} \right)^{2}} \right)}} - {10n\; {\log_{10}\left( \sqrt{\left( {x - x_{3}} \right)^{2} + \left( {y - y_{3}} \right)^{2}} \right)}}}$${{P_{r_{2}}\left( {{dB}\; m} \right)} - {P_{r_{3}}\left( {{dB}\; m} \right)}} = {\quad{{10n\; {\log_{10}\left( \sqrt{\left( {x - x_{3}} \right)^{2} + \left( {y - y_{3}} \right)^{2}} \right)}} - {10\; n\; {\log_{10}\left( \sqrt{\left( {x - x_{2}} \right)^{2} + \left( {y - y_{2}} \right)^{2}} \right)}}}\;}$

For geolocation systems and methods of the present invention, preferablytwo or more devices or units are used to provide nodes. More preferably,three devices or units are used together or “joined” to achieve thegeolocation results. Also preferably, at least three devices or unitsare provided. Software is provided and operable to enable anetwork-based method for transferring data between or among the at leasttwo device or units, or more preferably at least three nodes, a databaseis provided having a database structure to receive input from the nodes(transferred data), and at least one processor coupled with memory toact on the database for performing calculations, transforming measureddata and storing the measured data and statistical data associated withit; the database structure is further designed, constructed andconfigured to derive the geolocation of nodes from saved data and/orfrom real-time data that is measured by the units; also, the databaseand application of systems and methods of the present invention providefor geolocation of more than one node at a time. Additionally, softwareis operable to generate a visual representation of the geolocation ofthe nodes as a point on a map location.

Errors in measurements due to imperfect knowledge of the transmit poweror antenna gain, measurement error due to signal fading (multipath),interference, thermal noise, no line of sight (NLOS) propagation error(shadowing effect), and/or unknown propagation model, are overcome usingdifferential RSS measurements, which eliminate the need for transmitpower knowledge, and can incorporate TDOA and FDOA techniques to helpimprove measurements. The systems and methods of the present inventionare further operable to use statistical approximations to remove errorcauses from noise, timing and power measurements, multipath, and NLOSmeasurements. By way of example, the following methods are used forgeolocation statistical approximations and variances: maximum likelihood(nearest neighbor or Kalman filter); least squares approximation;Bayesian filter if prior knowledge data is included; and the like. Also,TDOA and FDOA equations are derived to help solve inconsistencies indistance calculations. Several methods or combinations of these methodsmay be used with the present invention, since geolocation will beperformed in different environments, including but not limited to indoorenvironments, outdoor environments, hybrid (stadium) environments, innercity environments, etc.

In recent years, demand for real-time information has increasedexponentially.

Consumers have embraced social media applications and there are now moremobile subscriptions than people on the planet. Studies show that atypical mobile device experiences an average of 10 network interactionsper minute (e.g., Facebook push, Twitter download). For example,Facebook on its own is driving 1 billion updates per minute. Rabidconsumer demand, combined with the growing needs of government andindustry (e.g., 2-way, trunked, IoT), translates into more wirelessactivities over wider frequency ranges. The activities are oftenintermittent with short durations of only a few hundred milliseconds oreven less than 100 milliseconds. Social media applications and othercellular activities (e.g., background refresh) are even shorter induration. Until now, the magnitude of activity has been impossible tokeep track of and even harder to gain intelligence from.

The present invention provides systems and methods for unmanned vehiclerecognition. The present invention relates to automatic signaldetection, temporal feature extraction, geolocation, and edge processingdisclosed in U.S. patent application Ser. No. 15/412,982 filed Jan. 23,2017, U.S. patent application Ser. No. 15/681,521 filed Aug. 21, 2017,U.S. patent application Ser. No. 15/681,540 filed Aug. 21, 2017, U.S.patent application Ser. No. 15/681,558 filed Aug. 21, 2017, each ofwhich is incorporated herein by reference in their entirety.

In one embodiment of the present invention, automatic signal detectionin an RF environment is based on power distribution by frequency overtime (PDFT), including the first derivative and the second derivativevalues. A PDFT processor is provided for automatic signal detection.

In one embodiment, the PDFT processor increments power values in a2-Dimentional (2D) array from a frequency spectrum over a set length oftime. The length of time is user-settable. For example, the length oftime can be set at 5 minutes, 1 hour, or 1 day. The length of time canbe set as low as 1 second. Typically, the smallest time interval forsetting the environment is 5 seconds. A histogram with frequency as thehorizontal axis and power as the vertical axis can be used to describepower values across a spectrum during a certain period of time, which iscalled the Power Bin Occurrence (PBO).

In one embodiment, power levels are collected for a specified length oftime, and statistical calculations are performed on the PBO to obtainthe power distribution by frequency for a certain time segment (PDFT).The statistical calculations create baseline signals and identify whatis normal in an RF environment, and what are changes to the RFenvironment. PBO data is constantly updated and compared to baseline todetect anything unique in the RF environment. In one embodiment, thePDFT processor is operable for data mining, including extracting,processing, and analyzing PBO datasets for statistical calculations.

The PDFT collects power values and describes the electromagneticenvironment with collected power values by frequency collected over thetime range of the collection. The electromagnetic environment includesinfrared radiation, visible lights, and RF spectrum. For example, thePDFT processor learns what should be present in the electromagneticenvironment in a certain area during the time segment from 3 pm to 5 pm.If there is a deviation from historical information, the PDFT processoris configured to send an alarm to operators.

In one embodiment, PBO is used to populate a 3-Dimentional (3D) arrayand create the Second Order Power Bin Occurrence (SOPBO). The timesegment of the PBO is a factor of the length of the SOPBO time segment.The first two dimensions are the same as in PBO, but the third dimensionin SOPBO describes how often the corresponding frequency bin and powerbin is populated over the SOPBO time segment. The result can bedescribed as a collection of several 2D histograms across a percent ofoccurrence bins such that each histogram represents a differentfrequency bin and power bin combination. This provides a percentage ofutilization of the frequency for non-constant signals such as RADAR,asynchronous data on demand links or push-to-talk voice. In oneembodiment, the percentage of utilization reflects if the spectrum isunder-utilized or over-utilized.

In one embodiment, the PBO, PDFT, and SOPBO data sets are used forsignal detection. For example, statistical calculations of PBOs during acertain time segment are used along with a set of detection parametersto identify possible signals. A frequency-dependent noise floor iscalculated by taking the spectral mean from the PDFT data and applying atype of median filter over subsets of frequency. For example, but notfor limitation, detection parameters include known signals, basiccharacteristics, databases of telecom signals, and etc. For example, butnot for limitation, median filter types include Standard Median Filter(MF), Weighted Median Filter (WMF), Adaptive Median Filter (AMF) andDecision Based Median Filter (DBMF). The noise floor is then assessedfor large changes in power, which indicates the noise floor values arefollowing the curvature of possible signals. At these frequencies, thenoise floor is adjusted to adjacent values. Power values below the noisefloor are ignored in the rest of the signal detection process. To detectsignals, the first derivative is calculated from a smoothed PDFTfrequency spectrum. Derivative values exceeding a threshold set based onthe detection parameters are matched to nearby values along thefrequency spectrum that are equal and opposite within a smalluncertainty level. Once frequency edges are found, power values are usedto further classify signals. The whole process including the noise floorcalculation is repeated for different time segments. The detectionparameters are adjusted over time based on signals found or not found,allowing the signal detection process to develop as the PDFT processorruns.

The first derivative of the FFT data is used to detect signals, measurepower, frequency and bandwidths of detected signals, determine noisefloor and variations, and classify detected signals (e.g., widebandsignals, narrowband signals). The second derivative of the FFT data isused to calculate velocity (i.e., change of power) and acceleration(i.e., rate of change of power), and identify movements based on changesand/or doppler effect. For example, the second derivative of the FFTdata in an RF environment can be used to determine if a signal emittingdevice is near road or moving with a car. A SOPBO is the secondderivative (i.e., a rate of change of power). The second derivativeshows if a signal varies over time. In one embodiment, the power levelof the signal varies over time. For example, a simplex network has basestation signals transmitting at certain time segments and mobile signalsin a different time segment. The SOPBO can catch the mobile signalswhile the first order PBO cannot. For signals that vary in time such asTime Division Duplex (TDD) LTE or a Radar, SOPBO is important.

FIG. 47 illustrates a configuration of a PDFT processor according to oneembodiment of the present invention. In one embodiment, a PDFT processorfor automatic signal detection comprises a management plane, at leastone RF receiver, a generator engine, and an analyzer engine. Themanagement plane is operable to configure, monitor and manage jobfunctions of the PDFT processor. The at least one RF receiver isoperable to receive RF data, generate I/Q data based on the received RFdata, and perform FFT analysis. The generator engine is configured toperform a PBO process, and generate PDFT data and SOPBO data based onPBO data. The analyzer engine is configured to calculate noise floor,smooth max hold, generate a PDFT baseline, and identify signals. Thesmooth max hold function is a curve fitting process with a partialdifferential equation to provide a running average across adjacentpoints to reject impulse noise that can be present in the FFT data. Theanalyzer engine is further configured to calculate a SOPBO baselinebased on the SOPBO data.

FIG. 48 is a flow chart for data processing in a PDFT processoraccording to one embodiment of the present invention. A job manifest iscreated for initial configuration of a PDFT generator engine or updatingthe configuration of the PDFT generator engine. The job manifest alsostarts an RF receiver to receive radio data from an RF environment. Thereceived radio data is transmitted to an FFT engine for FFT analysis.The PDFT generator engine pulls FFT data stream from the FFT engine tobuild up a based PBO and run a PBO process continuously. An SOPBOprocess and a PDFT process are performed based on PBO data. SOPBO datafrom the SOPBO process and PDFT data from the PDFT process is publishedand saved to storage. The data from the PDFT generator engine istransmitted to an PDFT analyzer engine for analytics including signaldetection and classification, event detection and environmentmonitoring, mask creation, and other analyzer services.

FIG. 49 illustrates data analytics in an analyzer engine according toone embodiment of the present invention. Classical RF techniques and newRF techniques are combined to perform data analytics includingenvironment monitoring and signal classification. Classical RFtechniques are based on known signals and initial parameters includingdemodulation parameters, prior knowledge parameters, and user providedparameters. New RF techniques use machine learning to learn signaldetection parameters and signal properties to update detectionparameters for signal classification. New signals are found and used toupdate learned signal detection parameters and taught signal propertiesbased on supervised and unsupervised machine learning.

In one embodiment, the automatic signal detection process includes maskcreation and environment analysis using masks. Mask creation is aprocess of elaborating a representation of an RF environment byanalyzing a spectrum of signals over a certain period of time. A desiredfrequency range is entered by a user to create a mask, and FFT streamingdata is also used in the mask creation process. A first derivative iscalculated and used for identifying maximum power values. A movingaverage value is created as FFT data is received during a time periodselected by the user for mask creation. For example, the time period is10 seconds. The result is an FFT array with an average of maximum powervalues, which is called a mask. FIG. 50 illustrates a mask according toone embodiment of the present invention.

In one embodiment, the mask is used for environment analysis. In oneembodiment, the mask is used for identifying potential unwanted signalsin an RF environment.

Each mask has an analysis time. During its analysis time, a mask isscanned and live FFT streaming data is compared against the mask beforenext mask arrives. If a value is detected over the mask range, a triggeranalysis is performed. Each mask has a set of trigger conditions, and analarm is triggered into the system if the trigger conditions are met. Inone embodiment, there are three main trigger conditions including alarmduration, db offset, and count. The alarm duration is a time window analarm needs to appear to be considered as one. For example, the timewindow is 2 seconds. If a signal is seen for 2 seconds, it passes to thenext condition. The db offset is the db value a signal needs to be abovethe mask to be considered as a potential alarm. The count is the numberof times the first two conditions need to happen before an alarm istriggered into the system.

FIG. 51 illustrates a workflow of automatic signal detection accordingto one embodiment of the present invention. A mask definition isspecified by a user for an automatic signal detection process includingcreating masks, saving masks, and performing environment analysis basedon the masks created and FFT data stream from a radio server. If triggerconditions are met, alarms are triggered and stored to a local databasefor visualization.

FIG. 52 is a screenshot illustrating alarm visualization via a graphicaluser interface (GUI) according to one embodiment of the presentinvention. In the GUI, current alarms, acknowledged alarms, anddismissed alarms in a certain RF environment are listed with informationincluding types, counts, durations, carrier frequencies, technologies,and band allocations.

In one embodiment, a detection algorithm is used for alarm triggering.The detection algorithm detects power values over the mask consideringthe db offset condition, but does not trigger an alarm yet. FIG. 53illustrates a comparison of live FFT stream data and a mask consideringa db offset according to one embodiment of the present invention. The dboffset is 5 db, so the detection algorithm only identifies power valuesthat are at least 5 db higher than the mask.

The detection algorithm then identifies peaks for power values above themask after considering the db offset. In embodiment of the presentinvention, a flag is used for identifying peak values. A flag is aBoolean value used for indicating a binary choice. FIG. 54 is a snippetof the code of the detection algorithm defining a flag according to oneembodiment of the embodiment. If the flag is TRUE, the detectionalgorithm keeps looking for peak values. A forEach function analyzeseach value to find the next peak. Once reaching a peak value, it goesdown to the value nearest to the mask, and the flag is set to FALSE.FIG. 55 is a snippet of the code of the detection algorithm identifyingpeak values according to one embodiment of the present invention.

In one embodiment, live FFT stream data has multiple peaks beforefalling under the mask. FIG. 56 illustrates a complex spectrum situationaccording to one embodiment of the present invention. Live FFT streamdata in two alarm durations have multiple peaks before falling under themask. FIG. 57 is an analysis of the live FFT stream data above the maskin the first alarm duration in FIG. 56 according to one embodiment ofthe present invention. A first peak is identified, and the power valuestarts to decrease. A first value nearest to the mask after the firstpeak is identified, the flag is still TRUE after comparing the firstvalue nearest to the mask and mask, so the detection algorithm keepslooking for peaks. Then, a second peak is identified, and the powervalue starts to decrease. A second value nearest to the mask after thesecond peak is identified. The second value is greater than the firstvalue, the flag is still TRUE, so the detection algorithm keeps lookingfor peak values. Then a third peak value is identified and a third valuenearest to the mask is also identified. The third value is on the maskconsidering the offset value, and the flag is set to FALSE. Bycomparison, the third peak value is considered as the real peak valuefor the power values above the mask in the first alarm duration of FIG.56. Once all the peaks are found, the detection algorithm checks thealarm duration, which is a time window where a signal needs to be seenin order to be considered for alarm triggering. The first time that thedetection algorithm sees the peak, it saves the time in memory. If thesignal is still present during the time window, or appears anddisappears during that time, the detection algorithm is to considertriggering an alarm. If the condition is not met, a real-time alarm isnot sent to a user, however the detected sequence is recorded for futureanalysis. FIG. 58 is a snippet of the code of the detection algorithmchecking the alarm duration according to one embodiment of the presentinvention.

If both the db offset condition and the alarm duration condition aremet, the detection algorithm analyzes the count condition. If the amountof times specified in the count condition is met, the detectionalgorithm triggers the alarm. In one embodiment, all alarms are returnedas a JSON array, and a forEach function creates the structure andtriggers the alarm. FIG. 59 is a snippet of the code of the detectionalgorithm triggering an alarm according to one embodiment of the presentinvention.

The present invention provides spectrum monitoring and management,spectrum utilization improvements, critical asset protection/physicalsecurity, interference detection and identification, real timesituational awareness, drone threat management, and signal intelligence(SigINT). Advantageously, the automatic signal detection in the presentinvention provides automated and real-time processing, environmentallearning, autonomous alarming and operations (e.g., direction finding,demodulation), wideband detection, etc. The automatic signal detectionin the present invention is of high speed and high resolution with lowbackhaul requirements, and can work in both portal and fixed modes withcell and land mobile radio (LMR) demodulation capability. The automaticsignal detection system in the present invention is operable tointegrate with third party architecture, and can be configured withdistributed architecture and remote management. In one embodiment, theautomatic signal detection of the present invention is integrable withany radio server including any radio and software defined radio, forexample, Ettus SDR radio products.

Specifically, spectrum solutions provided by the automatic signaldetection technology in the present invention have the followingadvantages: task automation, edge processing, high-level modulararchitecture, and wideband analysis.

Task automation simplifies the work effort required to perform thefollowing tasks, including receiver configuration, process flow andorchestration, trigger and alarm management, autonomous identificationof conflicts and anomalous signal detection, automated analytics andreporting, system health management (e.g., system issues/recovery,software update, etc.). FIG. 60 is a screenshot illustrating a jobmanager screen according to one embodiment of the present invention.FIG. 61 illustrates trigger and alarm management according to oneembodiment of the present invention.

Task automation enables an operator to send a job to one or multiplesystems distributed across a geography. Each job contains a pre-built,editable manifest, which can configure receivers and outline alarmconditions with appropriate actions to execute. As an example, for abaseline analysis task, the system automatically scans multiple blocksof spectrum in UHF, VHF, Telco bands and ISM bands such as 2.4 GHz and5.8 GHz, stores multiple derivatives regarding signal and noise flooractivity, produces an automated report showing activity and occupancyover a specified time, analyzes signal activity to correctly channelizeactivity by center frequency and bandwidth, and combines customersupplied or nationally available databases with data collected to addcontext (e.g., license, utilization, etc.). The baseline analysis taskprovides an operator with a view into a spectral environment regardingutilization and occupancy. This can be of assistance when multipleentities (local, state and federal agencies) have coverage during acritical event and need to coordinate frequencies. Multiple radios alongwith multiple systems across a geography can be commanded to begingathering data in the appropriate frequency bands. Resolution bandwidthand attenuation levels are adjustable, coordination is made simple, andactionable information is returned without significant manual effort.

The systems provided in the present invention is operable to processelectromagnetic (EM) data (e.g., RF data, infrared data, visible lightdata) and perform data manipulation directly at the sensor level. Alldata can be pushed to a server, but by processing the data first at thesensor, much like in IoT applications, more can be done with less.Overall, edge processing makes information more actionable and reducescost. The systems of the present invention also leverage machinelearning to drive automation at the edge to a higher level, which makessolutions provided by the present invention more intuitive, with greatercapability than other remote spectrum monitoring solutions. Edgeprocessing also reduces the bandwidth requirements for the network bydistilling data prior to transfer. A reduction in storage requirements,both on the physical system and for a data pipe, enables more deploymentoptions and strategies. For example, different deployment options andstrategies include vehicle mounted (e.g., bus or UPS trucks mapping ageography with cellular backhaul), transportable (e.g., placed in atower on a limited basis) where ethernet is not available, andman-portable (e.g., interactive unit connected to other mobile or fixedunits for comparative analysis).

Core capabilities processed on the node at the edge of the networkinclude spectrum reconnaissance, spectrum surveillance with tip and cue,and signal characterization. Spectrum reconnaissance includes automaticcapture and production of detail regarding spectrum usage overfrequency, geography and time. More actionable information is providedwith edge processing, distributed architecture and intelligent datastorage. Spectrum surveillance includes automated deconfliction overwidebands by comparing real-time data to user supplied, regional andlearned data sets and producing alarms. Nodes can also work with thirdparty systems, such as cameras, making them smarter. Signalcharacterization provides actionable information. signals of interestare decoded and demodulated by the system, with location approximationor direction, to improve situational intelligence.

In one embodiment, edge processing of the present invention includesfour steps. At step one, first and second derivative FFT analysis isperformed in near real time, providing noise floor estimates and signalactivity tracking. FIG. 62 is a screenshot illustrating a spectrum withRF signals and related analysis. FIG. 63 is a screenshot illustratingidentified signals based on the analysis in FIG. 62. Spectrum in theshaded areas in FIG. 63 are identified as signals. At step two, analysisis aggregated, signal bandwidths and overall structure are defined, anddata is stored to create baselines and be used in reporting. At stepthree, incoming FFT is compared to existing baselines to find potentialconflicts to the baseline. When conflicts are detected, parameters aresent to an event manager (e.g., a logic engine). At step four, the eventmanager utilizes user supplied knowledge, publicly available data, jobmanifests and learned information to decide appropriate actions. Actionrequests such as creating an alarm, sending an e-mail, storing I/Q data,or performing DF are sent to a controller.

A modular approach to system design and distributed computing allows forproper resource management and control when enabled by the right systemcontrol solution, which maximizes performance while keeping per-unitcost down. A loosely coupled solution architecture also allows for lesscostly improvements to the overall network. Parallel processing alsoenables multiple loosely coupled systems to operate simultaneouslywithout inhibiting each other's independent activities. FIG. 64 is adiagram of a modular architecture according to one embodiment of thepresent invention. A modular design enables different components to beintegrated and updated easily, without the need for costly customizationor the never-ending purchase of new equipment, and makes it easier toadd in additional hardware/software modules.

Compared to the industry standard tightly coupled architecturesincreasing complexity and reducing scalability, reliability and securityover time, the loosely coupled modular approach providesstandardization, consolidation, scalability and governance whilereducing cost of operation.

The spectrum monitoring solutions provided in the present inventionsignificantly enhance situational intelligence and physical security,reduces utility complexity and project risk.

The spectrum management systems provided in the present invention areoperable to detect and report on incidents in near real time. Remotesensors are placed at site with the capability of capturing andprocessing RF activity from 40 MHz to 6 GHz. Highly accurate baselinesare constructed for automated comparison and conflict detection. Systemsare connected to a centralized monitoring and management system,providing alarms with details to a network operations center. On-sitesystems can also provide messages to additional security systemson-site, such as cameras, to turn them to the appropriate azimuths.

In one embodiment, information such as the presence of a transmissionsystem can be used in an unmanned vehicle recognition system (UVRS) todetect the presence of an unmanned vehicle. The unmanned vehicle can beair-borne, land-based, water-borne, and/or submerged. The detection ofcertain modulation schemes can be used to identify the presence ofmobile phones or mobile radios. This information, coupled with directionfinding, provides situational intelligence for informed decision makingand rapid response. Measurements and signal intelligence regarding an RFspectrum assist in reducing the risk of financial losses due to theft,vandalism, and power disruptions, providing additional safety foremployees and visitors, making other security technologies, such asthermal cameras and IP videos smarter by working in tandem to identifyand locate the presence of threats, and capturing and storing complextime-based data, real-time data, and frequency-based data, which can beutilized as evidence for legal proceedings.

Wireless devices can be utilized across multiple bands. While othermonitoring systems are limited on bandwidth (i.e., limited focus) orresolution (making it difficult to see narrowband signals), the systemsin the present invention are designed to be more flexible and adaptableand capable of surveying the entire communications environments lookingfor illicit activity. FIG. 65 illustrates a communications environmentaccording to one embodiment of the present invention.

In one embodiment, a signal characterization engine is configured toprovide information including location information and direction,operator name, drone transmission type, and MAC address. All these areactionable information enabling swift resolution. FIG. 66 illustrates anUVRS interface with positive detections, according to one embodiment ofthe present invention. FIG. 67 lists signal strength measurementsaccording to one embodiment of the present invention.

In one embodiment, the systems of the present invention can be used formitigating drone threats, identifying and locating jammers, and ensuringcommunications. The systems of the present invention are designed toidentify illicit activity involving use of the electromagnetic spectrumsuch as drone threats, directed energy/anti-radiation weapons aimed atdegrading combat capability (e.g., jammers). The systems of the presentinvention also bring structure to largely unstructured spectral dataenabling clearer communications (interference reduction) and efficientcommunication mission planning.

Jammers are becoming more prevalent and can be deployed on-site or offpremises, making them very difficult to locate. The solutions providedby the present invention automatically send alerts as to the presence ofwideband jammers interfering with critical parts of the communicationsspectrum, and assist in the location of focused jammers which can bevery difficult to find. The ability to proactively and rapidly locatejamming devices reduces disruptions in communications, and improvesoverall security and limits the potential for financial loss. FIG. 68illustrates a focused jammer in a mobile application according to oneembodiment of the present invention. FIG. 69 illustrates a swept RFinterference by a jammer according to one embodiment of the presentinvention.

To maintain security and coordinate operations, consistent and qualitycommunications are imperative. The systems provided in the presentinvention have multiple deployment strategies and data can be collectedand distilled into strength and quality metrics. The data is easy toaccess in reports. FIG. 70 illustrates data collection, distillation andreporting according to one embodiment of the present invention.

The systems provided in the present invention have the capability ofbuilding baselines, detecting when signals exist which are not commonfor the environment, and creating alerts and automatically startingprocesses such as direction finding.

The systems provided in the present invention can be used for counteringunmanned vehicles, including but not limited to unmanned aerial systems,land-based vehicles, water-borne vehicles, and submerged vehicles. FIG.71 is a comparison of multiple methodologies for detecting andclassifying UAS. Of the methods listed in FIG. 72, RF detection providesthe highest level of accuracy in classifying an object as a UAS.

An RF-based counter-UAS system comprises multiple receivers in a singleplatform. In one embodiment, there are four receivers. Each receiver isoperable to scan multiple bands of spectrum looking for UAS signatures.For example, the multiple bands of spectrum include 433 MHz, 900 MHz,2.4 GHz, 3.5 GHz, and 5.8 GHz Base. Each receiver has the capability ofscanning a spectrum from 40 MHz to 6 GHz. The receivers are capable ofworking in tandem for DF applications. Multiple RF-based counter-UASsystems can communicate with each other to extend range of detection andenhance location finding accuracy. The RF-based counter-UAS systems ofthe present invention comprise proprietary intelligence algorithm on oneor multiple GPUs with execution time less than 10 ms. FIG. 72 listscapabilities of an RF-based counter-UAS system according to oneembodiment of the present invention. The capabilities of an RF-basedcounter-UAS system include detection, classification, direction finding,and message creation.

In one embodiment, an RF-based counter-UAS systems can be deployed as along-distance detection model as illustrated in FIG. 73. Fouromni-directional antennas are used to create an array for detection anddirection finding. In one embodiment, Gimbal-mounted (rotating) defeatantenna can be added. The long-distance detection model is simple toinstall. In one embodiment, extremely long-distance detection can beobtained with arrays utilizing masts with a height of 8 to 10 meters.

FIG. 74 illustrates features of drones in the OcuSync family. FIG. 75illustrates features of drones in the Lightbridge family. The longranges, adaptability, and ubiquity of OcuSync and Lightbridge systemsmake them potentially very dangerous. The RF-based counter-UAS systemsin the present invention are operable to detect and defeat UASs usingthese systems.

The RF-based counter-UAS systems in the present invention are operableto detect UASs over a distance of 1.5 kilometers with direction. UASscan be detected and categorized faster than other systems. The RF-basedcounter-UAS systems can easily integrated into third party active andpassive systems (e.g., RADAR and camera systems), or act as the commonoperating platform for other systems for command and control. TheRF-based counter-UAS systems are capable for wideband detection from 70MHz to 6 GHz, enabling detection of UASs at 433 MHz, 900 MHz, 2.4 GHz,3.5 GHz, and 5.8 GHz. The RF-based counter-UAS systems are capable ofdetecting and direction finding UAS controllers. In one embodiment,unknown and anomalous signals can be categorized as UAS.

In one embodiment, the RF-based counter-UAS systems in the presentinvention can be used for detecting other unmanned vehicles such asland-based, water-borne, or submerged unmanned vehicles in addition todetecting unmanned aerial vehicles.

In one embodiment, the present invention provides an autonomous andintelligent spectrum monitoring system capable of detecting the presenceof wireless activity across extremely wide bands, capturing andperforming analysis on highly intermittent signals with short durationsautomatically, and converting RF data from diverse wireless mobilecommunication services (e.g., cellular, 2-way, trunked) into knowledge.The autonomous and intelligent spectrum monitoring system of the presentinvention are advantageous with edge processing, modular architecture,job automation, and distributed sensor network.

Edge processing enables the delivery of a truly autonomous sensor forautomated signal recognition and classification and near real-timealarming 24/7, equipped with machine learning algorithms.

A modular architecture increases speed and efficiency, enables morebandwidth to be analyzed (with superior resolution), reduces latency andnetwork traffic (i.e., low backhaul requirements). Logic engines producerelevant alarms, thus limiting false positives.

Job automation allows hardware solutions to be customized to meetoperational needs with inclusion of additional receivers and GPUs, cloudor client hosted backend, and third-party integration.

A distributed sensor network supports feature specific applications suchas direction finding and drone threat management, capable of LMR andcellular demodulation and assisting prosecution efforts with datastorage.

The spectrum monitoring system of the present invention represents aparadigm shift in spectrum management. Edge processing migrates awayfrom the inefficiencies of manual analysis, or the time delays ofbackhauling large data sets. The spectrum monitoring system of thepresent invention performs real-time, automated processing at the devicelevel, providing knowledge faster, reducing network traffic andimproving application performance with less latency. Modulararchitecture makes additional development, integration of new featuresand the incorporation of third party systems easy, and also future-proofcapital expenditure. Job automation simplifies operations (e.g., datacollection, setting triggers) by enabling the execution of multiplecomplex tasks, with one click on a user interface. Distributed sensorsprovide security to critical assets spread across large geographies,linked to a network operations center. Data can be shared to performlocation finding and motion tracking.

For critical assets, only certain types of transmitting devices (e.g.,radios, phones, sensors) should be present on specified frequencies. Thespectrum monitoring system of the present invention learns what iscommon for a communications environment and creates alarms when ananomalous signal is detected in close proximity. Alerts, along withdetails such as signal type (e.g., LMR, Mobile, Wi-Fi) and uniquecharacteristics (e.g., radio ID) are posted to a remote interface forfurther investigation. The spectrum monitoring system of the presentinvention which is capable of learning, analyzing and creating alarmsautonomously provides a heightened level of security for critical assetsand infrastructure. FIG. 76 illustrates a spectrum monitoring systemdetecting an anomalous signal in close proximity of criticalinfrastructure.

The spectrum monitoring system derives intelligence by collecting,processing, and analyzing spectral environments in near real time. Theunique characteristics and signatures of each transmitter are comparedautomatically to either user supplied or historical data sets. Potentialthreats are identified quickly and proactively, reducing acts ofvandalism, theft and destruction. Advantageously, the spectrummonitoring system of the present invention reduces the risk of financiallosses due to theft, vandalism, and power disruptions, providesadditional safety for employees and visitors, makes other securitytechnologies including thermal cameras and IP video smarter by workingin tandem to identify and locate the presence of threats (with DFfunctionality), and captures and stores data, which can be utilized asevidence for legal proceedings.

Node devices in the spectrum monitoring system of the present inventioncan be deployed across large geographies. The spectrum monitoring systemis built to interact with third party systems including cameras and bigdata platforms, providing additional intelligence. All these systemssend pre-processed data to a cloud platform and are visualizedefficiently on a single interface. FIG. 77 illustrates a systemconfiguration and interface according to one embodiment of the presentinvention.

Alarms generated at the site are sent to a remote interface, enablingperimeters to be monitored 24/7 from anywhere. Alarm details includingtransmitter type (e.g., mobile phone), unique identifiers (e.g., radioID), UAV type, and directions are presented on the interface.

Job automation restructures work flow and the need for configurationmanagement, greatly reducing manual efforts regarding receiverconfiguration, trigger and alarm management, analytics and reporting,system health management, and conflict and anomalous signal detection.

Not all activity observed in a spectral environment represents a threat.Even in remote locations, LMR radios can be observed. Pedestrians mayalso be in the area utilizing mobile devices. The spectrum monitoringsystem of the present invention is equipped with logic to determine thetypical makeup of an environment (e.g., common signals based on time ofday), proximity, and duration (e.g., time on site). The logic limitsfalse positives to produce alarms that are meaningful. Parameters can beadjusted as required.

FIG. 78 is a screenshot illustrating no alarm going off for an anomaloussignal from LMR traffic not in proximity of the site according to oneembodiment of the present invention. The signal at 467.5617 MHz and−73.13 dBm does not cause an alarm to go off.

In one embodiment, the spectrum monitoring system of the presentinvention enables 24/7 scanning of a local environment, identificationof new activities (e.g., LMR, cellular, Wi-Fi), threat assessmentcapability (e.g., proximity and duration analysis), and alarm creationwith details sent via email and posted to a user interface.

In one embodiment, the spectrum monitoring system of the presentinvention supports a powerful user interface simplifying remotemonitoring, greatly improves receiver sensitivity and processingenabling identification of intermittent signals with milliseconddurations (e.g., registration events, WhatsApp messaging, backgroundapplications), and provides an enhanced logic engine which is operableto identify both signals with long durations (e.g., voice calls, videostreaming, data sessions) and repetitive short bursts (e.g., Facebookupdates).

In one embodiment, the spectrum monitoring system of the presentinvention is capable of mobile phone identification from 800-2600 MHz(covering all mobile activity at site), recognition of intermittent andbursting signals associated with cellular applications, identificationof LMR, Wi-Fi, and UAV activity, and determining proximity and limitingfalse alarms with logic engines.

Node devices in a spectrum monitoring system of the present inventionare operable to produce data sets tagged with geographical node locationand time. The data sets can be stored on the node devices, or fed to acloud-based analytics system for historical trend analysis, predictionmodels, and customer driven deep learning analytics.

Analytics provided by the spectrum monitoring system of the presentinvention can be used to identify the presence of constant or periodicsignals. For example, recognition of the presence of wireless camerascan indicate potential surveillance of a critical asset site. Also forexample, the presence of constant or periodic signals can indicateexistence of organized groups, attempting to determine normal accesspatterns for the purpose of espionage or theft.

Analytics provided by the spectrum monitoring system of the presentinvention can also be used to review patterns before and during anintrusion at several sites and predict next targeted sites.

Analytics provided by the spectrum monitoring system of the presentinvention can also be used to track contractor and employee visits, bothplanned and unplanned to the site, to augment data for work flowimprovements.

FIG. 79 illustrates a GUI of a remote alarm manager according to oneembodiment of the present invention.

FIG. 80 labels different parts of a front panel of a spectrum monitoringdevice according to one embodiment of the present invention.

FIG. 81 lists all the labels in FIG. 79 representing different part ofthe front panel of the spectrum monitoring device according to oneembodiment of the present invention.

FIG. 82 illustrates a spectrum monitoring device scanning a spectrumfrom 40 MHz to 6 GHz according to one embodiment of the presentinvention.

FIG. 83 lists the capabilities of a spectrum monitoring system accordingto 5 main on-network mobile phone states plus 1 no-network mobile phonestate.

A mobile phone in the first main state is active on network, andactivities also include short-duration (e.g., milliseconds) activities(e.g., text messages, WhatsApp messages and registration events) besidescompleting a voice call, engaging in a data session, and streamingvideo. The first main state lasts 6 to 8 hours typically. Receiversensitivity for speed and bandwidth and processing are enhanced toenable the capability of intercepting these activities and producing analarm by the spectrum monitoring system of the present invention.

In the second main state, there are background applications running. Toconserve batter life, a mobile phone does not constantly monitor thenetwork, but does “wake up” and check for messages (e.g., every 10seconds). The mobile phone checks applications including Facebook, SMS,voicemail, email, Twitter, and game challenge notifications. A typicalphone sends an update notice (e.g., a request to pull down emails,Facebook messages, etc.) every 90 seconds on average. Backgroundapplications such as social media updates are extremely short induration. To capture these events, receivers in the spectrum monitoringsystem are doubled (e.g., 2 to 4), the bandwidth of each receiver isdoubled (e.g., 40 MHz to 80 MHz), and software is developed to enhancethe system to process the increase in sample (e.g., 10×).

FIG. 84 illustrates a mobile event analysis per one minute intervalsaccording to one embodiment of the present invention.

Events on a mobile phone include background apps (e.g., Facebook, Email,location services, sync apps) with a probability of 90%, active apps(e.g., mobile search, gaming) with a probability of 30%, messaging(e.g., SMS, WhatsApp, Snapchat) with a probability of 15%, voice callswith a probability of 10%. The combined probability gets to 95%.

FIG. 85 is a site cellular survey result according to one embodiment ofthe present invention. The site cellular survey result reveals there isnot active GSM network on site, which means the vast majority of themobile phones need to be UMTS and LTE capable to have service.

In one embodiment, the spectrum management node device is in networkcommunication with at least one camera for sensing and locating unmannedaerial vehicles (e.g., drones) in an electromagnetic environment acrossa wide spectrum from 30 Hz to 3 THz including RF spectrum, visiblelights, and infrared radiation. In one embodiment, the at least onecamera includes multiple lenses with a focal length which allows imagefocus up to 2 kilometers. In one embodiment, there are 19 lenses in acamera, and the synthesized aperture is between 70 degrees and 150degrees. The at least one camera is mounted on a defend gimbal. In oneembodiment, the at least one camera is stationary. In anotherembodiment, the at least one camera has pan/tilt/zoom feature. Imagesfrom the multiple lenses are stitched together as video data and relayedto the spectrum management node device. In one embodiment, the multiplelenses in a camera work individually. For example, there are 19 videofeeds from all the 19 lenses in a camera, which are to be processedindependently. In another embodiment, the multiple lenses work ingroups. For example, the 19 lenses in a camera work in two or threegroups, and only two or three video feeds from the two or three groupsare processed. In one embodiment, there are up to 20 lenses in a camera.In one embodiment, a server platform is operable to process one videofeed with 4 video processing cards, which is considered as basicprocessing power. When there are 2 video feeds, the processing power ofthe server platform needs to be 4 times the basic processing power. Whenthere are 4 video feeds, the processing power of the server platformneeds to be 20 times the basic processing power. In one embodiment, theserver platform is operable to process video feeds from up to 500lenses.

With prior art camera systems, it is nearly impossible to detect dronesand other unmanned vehicles in real time or near real time because ofthe huge amount of video feeds from various angles and directions in anEM environment. The present invention leverages RF analytics data toprovide a focus area of the camera systems. Thus, the RF analytics dataprovides a reference point with distance and angle for the camerasystems on a map to point to. The camera system then adjusts its focallength and frame of reference to detect drones or other unmannedvehicles. The camera system can detect if there is only one drone or aswarm of drones in the direction provided by the RF analytics data. Inone embodiment, the present invention is operable to lock the camerasystem onto the one or more detected drones or unmanned vehicles withouthuman beings looking at them.

In one embodiment, the spectrum management node device comprises a videoanalytics module. In one embodiment, the video analytics module isoperable to analyze the video data based on an artificial intelligence(AI) algorithm. In one embodiment, the video analytics module applies amodified You Only Look Once (YOLO) algorithm for detecting and groupingbounding boxes of drones identified in an image.

In one embodiment, the video analytics module is operable to determine adistance of an unmanned aerial vehicle and an inclination/declination ofthe unmanned aerial vehicle. The video analytics module is furtheroperable to classify unmanned aerial vehicle types and detect futurepayload (e.g., is there any bomb or aerosol dispenser attached to adrone?). In one embodiment, the video analytics module is furtheroperable to determine if an unmanned aerial vehicle is utilizing morethan one camera based on the video data combined with RF analytics data.In one embodiment, the video analytics module is further operable todetermine if there are multiple drones in the monitored RF environmentwhen the RF analytics detects multiple drone controller signals and/orwhen the RF analytics detects multiple drone radio signals. In oneembodiment, the video analytics module is further operable to detect ifthe multiple drones are on the same flight path, and if the multipledrones are in a tethered operation or non-tethered operation.

By combining the video analytics and the RF analytics, the spectrummanagement node device is operable to measure deterministicapproximations of the flight path of multiple drones or swarms ofdrones, which aides in determining posture and threat categorization.

In one embodiment, the video analytics module of the spectrum managementnode device is operable to track the formation of multiple drones, anddetermines if the multiple drones are in a single formation or multipleformations. Multiple formations of multiple non-tethered drones indicatethere are multiple controllers with ability to operate independently.

In one embodiment, the video analytics module is operable to detecthumans, animals, and land-based large vehicles from the video data. Inone embodiment, the video analytics module is operable for facialrecognition for known database lookups. In one embodiment, the videoanalytics module is operable for reading license plate and queryingdatabases.

In one embodiment, the spectrum management node device is operable toassist a single camera or a camera system to detect and track a drone byidentifying a location in the sky to monitor based on direction findingand line of bearing. Thus, the camera system is provided with points ofreference not existing previously, the accuracy of drone identificationis increased, and single-drone and multi-drone tracking abilities areenhanced.

In one embodiment, spectrum management node devices are in networkcommunication with multiple cameras of single or multiple lenses in acluster layout, which is operable to cover a large facility and multiplethreats simultaneously.

FIG. 86 illustrates a system of a spectrum management node device innetwork communication with a video sensor according to one embodiment ofthe present invention. The spectrum management node device, comprisingan RF analytics module, a DF module, and a video analytics module, is innetwork communication with at least one video sensor. The RF analyticsmodule is operable to analyze RF data and detect drone related signals.The DF module is operable to determine a line of bearing of a detecteddrone. The video analytics module is operable to receive video datastreaming from the video sensor and analyze the video data for dronedetection and location. The video analytics module is further operableto control the video sensor based on the analytics to enhance the videosensor tracking abilities.

FIG. 87 is a diagram of a cluster layout for multiple systemsillustrated in FIG. 86 according to one embodiment of the presentinvention. Four RF/video systems are in network communication with amulti-node analytics and control platform. Each RF/video systemcomprises a spectrum management node device and at least one videosensor. The multi-node analytics and control platform provides a portalwith human user interface. The multi-node analytics and control platformis accessible via an authorized third-party or proprietary single paneof glass user interface.

In one embodiment, an orchestrator is in network communication with aspectrum management device and a camera system to orchestrate orcoordinate the activities of the spectrum management device and thecamera system. In one embodiment, the orchestrator is a module in thespectrum management device. In one embodiment, the orchestrator is anindividual device between the spectrum management device and the camerasystem.

In one embodiment, there is an orchestrator the spectrum management nodedevices and the camera systems in communication with the multi-nodeanalytics and control platform. In one embodiment, the orchestrator is amodule on the multi-node analytics and control platform. In oneembodiment, the orchestrator is an individual device.

When a camera system detects one or more drones, it feeds theinformation related to the one or more drones to the spectrum managementdevice and/or the analytics and control platform, including distancedata and angle data. The information is organized into a vectorincluding exact altitude data and a reference point on earth for adetected drone. The detected drones are illustrated in one or morebounding boxes in a video image dynamically. The orchestrator isoperable to orchestrate the process in the spectrum management nodedevices and that in the camera systems so that the spectrum managementnode devices monitor the environment for new threats and the camerasystems focus on existing detected threats. In one embodiment, thespectrum management node devices include 12 RF sensor monitoring theenvironment and detecting signals of interest, and there are 6 camerasdetecting and monitoring unmanned vehicles related to the signals ofinterest in the environment.

In one embodiment, the present invention is operable to detect unmannedvehicles (UVs) and corresponding controllers outside of a UV librarystored in the spectrum management device and/or the analytics andcontrol platform. The PDFT process in a spectrum management node deviceis operable to detect RF signals in an RF environment. One dronecontroller may emit signals hopping between different frequency bands orchannels, with different modulations and/or protocols. Video signalsfrom the camera sensors are operable to detect drones and other unmannedvehicles in the environment. In one embodiment, the video signals have10-20 MHz bandwidth. In one embodiment, the video signals are notcontinuous videos but groups of frames. In one embodiment, the videosignals are compressed and interleaved to correlate with RF signals fordrone detection and monitoring. In one embodiment, the present inventionis operable to detect UVs outside the normal RF detection range byorchestrating and correlating video signals and RF signals. For example,the present invention is operable to detect a drone controller 4kilometers away from a detected drone.

In one embodiment, the present invention provides systems, methods andapparatus for detecting unmanned aerial vehicles in an RF environment.An apparatus comprises at least one RF receiver, an RF analytics module,a direction finding (DF) module, and a video analytics module. Theapparatus is in network communication with at least one video sensor.The at least one video sensor is configured to capture images of the RFenvironment and stream video data to the apparatus. The at least one RFreceiver is configured to receive RF data and generate fast Fouriertransform (FFT) data based on the RF data. The RF analytics module isconfigured to identify at least one signal based on a first derivativeand a second derivative of the FFT data. The DF module is configured tomeasure a direction from which the at least one signal is transmitted.The DF module is also configured to locate signal transmitters bymeasuring Time-Difference-of-Arrival (TDoA), Power-Difference-of-Arrival(PDoA) or Angle-of-Arrival (AoA). The video analytics module isconfigured to analyze the video data, thereby creating analyzed videodata, identify at least one unmanned aerial vehicle to which the atleast one signal is related based on the analyzed video data, the RFdata, and the direction from which the at least one signal istransmitted, and control the at least one video sensor based on theanalyzed video data.

In one embodiment, the present invention provides a fixed nodal networkfor monitoring and managing electromagnetic spectrum for wirelesscommunications in a city area or a populated area. In the city orpopulated areas, many signals have high frequency (e.g., 5.8 GHz andabove), low power, and short reception radius (e.g., less than 1kilometers). The fixed nodal network comprises a multiplicity ofsensor-based node devices in mesh network communication.

In one embodiment, the present invention provides a fixed nodal networkfor monitoring and managing electromagnetic spectrum for wirelesscommunications in a smart city. In one embodiment, a multiplicity ofnode devices is fixed in or on street light boxes. In one embodiment,the multiplicity of node devices is fixed on cellular base stations(i.e., cell towers), including but not limited to macrocell basestations and small cell base stations. The small cells include but notlimited to femtocells, picocells, and microcells.

The present invention provides systems, methods and apparatus forautomatic signal detection in a radio-frequency (RF) environment. Atleast one node device is in a fixed nodal network. The at least one nodedevice comprises at least one receiver and at least one processorcoupled with at least one memory. The at least one node device isoperable to measure and learn the RF environment in a predeterminedperiod based on statistical learning techniques, thereby creatinglearning data. The at least one node device is operable to create aspectrum map based on the learning data. The at least one node device isoperable to calculate a power distribution by frequency of the RFenvironment in real time or near real time, including a first derivativeand a second derivative of fast Fourier transform (FFT) data of the RFenvironment. The at least one node device is operable to identify atleast one signal based on the first derivative and the second derivativeof FFT data. In one embodiment, the at least one node device is furtheroperable to communicate with a remote server platform. The remote serverplatform is operable to perform spectrum analytics based on data fromthe at least one node device, and display the spectrum map via agraphical user interface (GUI). In one embodiment, the at least onesignal has a frequency equal to or higher than 5.8 GHz. In oneembodiment, the at least one node device is operable to detect alocation of a signal transmitter of the at least one signal based onTime-Difference-of-Arrival (TDoA), Power-Difference-of-Arrival (PDoA)and/or Angle-of-Arrival (AoA). In one embodiment, the at least one nodedevice is fixed on at least one street light box. In one embodiment, theat least one node device is fixed on at least one cellular base stationcomprising at least one macrocell base station and/or at least one smallcell base station.

In one embodiment, the at least one node device is operable to displaythe spectrum map via a graphical user interface (GUI). In oneembodiment, the at least one node device is operable to calculate apercentage of spectrum utilization in the RF environment, and determineif the RF spectrum is under-utilized or over-utilized based on thepercentage of spectrum utilization in the RF environment.

In one embodiment, the at least one processor comprises an RF analyticsmodule operable for spectrum analytics based on an artificialintelligence algorithm. The at least one processor also comprises adirection-finding module operable for identifying a direction from whichthe at least one signal is transmitted based onTime-Difference-of-Arrival (TDoA), Power-Difference-of-Arrival (PDoA)and/or Angle-of-Arrival (AoA).

In one embodiment, the at least one node device is operable to detectinterferences in the electromagnetic environment. In one embodiment, theat least one node device is operable to identify at least one whitespace in the electromagnetic environment.

The foregoing method descriptions and the process flow diagrams areprovided merely as illustrative examples and are not intended to requireor imply that the steps of the various embodiments must be performed inthe order presented. As will be appreciated by one of skill in the artthe order of steps in the foregoing embodiments may be performed in anyorder. Words such as “thereafter,” “then,” “next,” etc. are not intendedto limit the order of the steps; these words are simply used to guidethe reader through the description of the methods. Further, anyreference to claim elements in the singular, for example, using thearticles “a,” “an” or “the” is not to be construed as limiting theelement to the singular.

The various illustrative logical blocks, modules, circuits, andalgorithm steps described in connection with the embodiments disclosedherein may be implemented as electronic hardware, computer software, orcombinations of both. To clearly illustrate this interchangeability ofhardware and software, various illustrative components, blocks, modules,circuits, and steps have been described above generally in terms oftheir functionality. Whether such functionality is implemented ashardware or software depends upon the particular application and designconstraints imposed on the overall system. Skilled artisans mayimplement the described functionality in varying ways for eachparticular application, but such implementation decisions should not beinterpreted as causing a departure from the scope of the presentinvention.

The hardware used to implement the various illustrative logics, logicalblocks, modules, and circuits described in connection with the aspectsdisclosed herein may be implemented or performed with a general purposeprocessor, a digital signal processor (DSP), an application specificintegrated circuit (ASIC), a field programmable gate array (FPGA) orother programmable logic device, discrete gate or transistor logic,discrete hardware components, or any combination thereof designed toperform the functions described herein. A general-purpose processor maybe a microprocessor, but, in the alternative, the processor may be anyconventional processor, controller, microcontroller, or state machine. Aprocessor may also be implemented as a combination of computing devices,e.g., a combination of a DSP and a microprocessor, a plurality ofmicroprocessors, one or more microprocessors in conjunction with a DSPcore, or any other such configuration. Alternatively, some steps ormethods may be performed by circuitry that is specific to a givenfunction.

In one or more exemplary aspects, the functions described may beimplemented in hardware, software, firmware, or any combination thereof.If implemented in software, the functions may be stored as one or moreinstructions or code on a non-transitory computer-readable medium ornon-transitory processor-readable medium. The steps of a method oralgorithm disclosed herein may be embodied in a processor-executablesoftware module which may reside on a non-transitory computer-readableor processor-readable storage medium. Non-transitory computer-readableor processor-readable storage media may be any storage media that may beaccessed by a computer or a processor. By way of example but notlimitation, such non-transitory computer-readable or processor-readablemedia may include RAM, ROM, EEPROM, FLASH memory, CD-ROM or otheroptical disk storage, magnetic disk storage or other magnetic storagedevices, or any other medium that may be used to store desired programcode in the form of instructions or data structures and that may beaccessed by a computer. Disk and disc, as used herein, includes compactdisc (CD), laser disc, optical disc, digital versatile disc (DVD),floppy disk, and blu-ray disc where disks usually reproduce datamagnetically, while discs reproduce data optically with lasers.Combinations of the above are also included within the scope ofnon-transitory computer-readable and processor-readable media.Additionally, the operations of a method or algorithm may reside as oneor any combination or set of codes and/or instructions on anon-transitory processor-readable medium and/or computer-readablemedium, which may be incorporated into a computer program product.

Certain modifications and improvements will occur to those skilled inthe art upon a reading of the foregoing description. The above-mentionedexamples are provided to serve the purpose of clarifying the aspects ofthe invention and it will be apparent to one skilled in the art thatthey d₀ not serve to limit the scope of the invention. All modificationsand improvements have been deleted herein for the sake of concisenessand readability but are properly within the scope of the presentinvention.

The invention claimed is:
 1. A system for automatic signal detection ina radio-frequency (RF) environment, comprising: at least one node devicein a fixed nodal network; wherein the at least one node device comprisesat least one receiver and at least one processor coupled with at leastone memory; wherein the at least one node device is operable to measureand learn the RF environment in a predetermined period based onstatistical learning techniques, thereby creating learning data; whereinthe at least one node device is operable to create a spectrum map basedon the learning data; wherein the at least one node device is operableto calculate a power distribution by frequency of the RF environment inreal time or near real time, including a first derivative and a secondderivative of fast Fourier transform (FFT) data of the RF environment;and wherein the at least one node device is operable to identify atleast one signal based on the first derivative and the second derivativeof FFT data.
 2. The system of claim 1, wherein the at least one nodedevice is further operable to communicate with a remote server platform.3. The system of claim 2, wherein the remote server platform is operableto perform spectrum analytics based on data from the at least one nodedevice.
 4. The system of claim 2, wherein the remote server is operableto display the spectrum map via a graphical user interface (GUI).
 5. Thesystem of claim 1, wherein the at least one signal has a frequency equalto or higher than 5.8 GHz.
 6. The system of claim 1, wherein the atleast one node device is operable to detect a location of a signaltransmitter of the at least one signal based onTime-Difference-of-Arrival (TDoA), Power-Difference-of-Arrival (PDoA)and/or Angle-of-Arrival (AoA).
 7. The system of claim 1, wherein the atleast one node device is fixed on at least one street light box.
 8. Thesystem of claim 1, wherein the at least one node device is fixed on atleast one cellular base station comprising at least one macrocell basestation and/or at least one small cell base station.
 9. The system ofclaim 1, wherein the at least one node device is mobile.
 10. The systemof claim 1, wherein the at least one node device is portable and/ortransportable.
 11. The system of claim 1, wherein the at least one nodedevice is operable to display the spectrum map via a graphical userinterface (GUI).
 12. The system of claim 1, wherein the at least onenode device is operable to calculate a percentage of spectrumutilization in the RF environment.
 13. The system of claim 12, whereinthe at least one node device is further operable to determine if the RFspectrum is under-utilized or over-utilized based on the percentage ofspectrum utilization in the RF environment.
 14. An apparatus forautomatic signal detection in a radio-frequency (RF) environment,comprising: at least one receiver and at least one processor coupledwith at least one memory; wherein the apparatus is an edge node of acommunication network; wherein the apparatus is operable to measure andlearn the RF environment in a predetermined period based on statisticallearning techniques, thereby creating learning data; wherein theapparatus is operable to create a spectrum map based on the learningdata; wherein the apparatus is operable to calculate a powerdistribution by frequency of the RF environment in real time or nearreal time; and wherein the apparatus is operable to identify at leastone signal based on the power distribution and the spectrum map of theRF environment.
 15. The apparatus of claim 14, wherein the at least oneprocessor comprises an RF analytics module operable for spectrumanalytics based on an artificial intelligence algorithm.
 16. Theapparatus of claim 14, wherein the at least one processor comprises adirection-finding module operable for identifying a direction from whichthe at least one signal is transmitted based onTime-Difference-of-Arrival (TDoA), Power-Difference-of-Arrival (PDoA)and/or Angle-of-Arrival (AoA).
 17. A method for automatic signaldetection in an electromagnetic environment, comprising: providing amultiplicity of node devices constructed and configured for mesh networkcommunication in the electromagnetic environment; the multiplicity ofnode devices measuring and learning the electromagnetic environment in apredetermined period based on statistical learning techniques, therebycreating learning data; the multiplicity of node devices creating aspectrum map based on the learning data; the multiplicity of nodedevices calculating a power distribution by frequency of theelectromagnetic environment in real time or near real time; and themultiplicity of node devices identifying at least one signal based onthe power distribution and the spectrum map of the electromagneticenvironment.
 18. The method of claim 17, further comprising themultiplicity of node devices detecting interferences in theelectromagnetic environment.
 19. The method of claim 17, furthercomprising the multiplicity of node devices identifying at least onewhite space in the electromagnetic environment.